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Goodman is PI and Director of the BEACON Center for the Study of Evolution in Action, an NSF Science and Technology Center headquartered at Michigan State University, funded at $47.5 million for 2010-20, with a diverse research program and extensive education and outreach programs. BEACON now has a very diverse membership of over 600, including evolutionary biologists and computer scientists/engineers studying evolutionary computation or digital evolution. Goodman received the Ph.D., computer and communication sciences, University of Michigan, 1972. He joined MSU’s faculty in Electrical Engineering and Systems Science in 1971, was promoted to full professor in 1984, and also holds appointments in Mechanical Engineering and in Computer Science and Engineering, in which he has guided many Ph.D. students. He directed the Case Center for Computer-Aided Engineering and Manufacturing from 1983-2002, and founded and directed MSU’s Manufacturing Research Consortium from 1993-2003. He co-founded MSU’s Genetic Algorithms Research and Applications Group (GARAGe) in 1993, conducting many projects under industrial sponsorship. In 1999, he co-founded Red Cedar Technology, Inc., (now a subsidiary of Siemens) which develops design optimization software, and was Vice President for Technology until BEACON was founded in 2010. He was chosen Michigan Distinguished Professor of the Year, 2009, by the Presidents Council, State Universities of Michigan. He was given MSU’s Distinguished Faculty Award in 2011. He was Chair of the Executive Board and a Senior Fellow of the International Society for Genetic and Evolutionary Computation, 2003-2005, and was the founding chair of ACM’s SIG on Genetic and Evolutionary Computation (SIGEVO) in 2005. He also co-leads an information and communication technology for development project in Tanzania.","image":"https://0.academia-photos.com/32766763/17997326/18006006/s200_eric.goodman.jpg","thumbnailUrl":"https://0.academia-photos.com/32766763/17997326/18006006/s65_eric.goodman.jpg","primaryImageOfPage":{"@type":"ImageObject","url":"https://0.academia-photos.com/32766763/17997326/18006006/s200_eric.goodman.jpg","width":200},"sameAs":[],"relatedLink":"https://www.academia.edu/126783026/MOEA_D_with_Angle_based_Constrained_Dominance_Principle_for_Constrained_Multi_objective_Optimization_Problems"}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/heading-95367dc03b794f6737f30123738a886cf53b7a65cdef98a922a98591d60063e3.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-8c9ae4b5c8a2531640c354d92a1f3579c8ff103277ef74913e34c8a76d4e6c00.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-170d1319f0e354621e81ca17054bb147da2856ec0702fe440a99af314a6338c5.css" /><link crossorigin="" href="https://fonts.gstatic.com/" rel="preconnect" /><link href="https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..1000&family=Gupter:wght@400;500;700&family=IBM+Plex+Mono:wght@300;400&family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@20,400,0,0&display=swap" rel="stylesheet" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/common-2b6f90dbd75f5941bc38f4ad716615f3ac449e7398313bb3bc225fba451cd9fa.css" /> <meta name="author" content="erik goodman" /> <meta name="description" content="Erik D. 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Goodman is PI and Director of the BEACON Center for the Study of Evolution in Action, an NSF Science and Technology Center headquartered at Michigan State University, funded at $47.5 million for 2010-20, with a diverse research program and extensive education and outreach programs. BEACON now has a very diverse membership of over 600, including evolutionary biologists and computer scientists/engineers studying evolutionary computation or digital evolution. Goodman received the Ph.D., computer and communication sciences, University of Michigan, 1972. He joined MSU’s faculty in Electrical Engineering and Systems Science in 1971, was promoted to full professor in 1984, and also holds appointments in Mechanical Engineering and in Computer Science and Engineering, in which he has guided many Ph.D. students. He directed the Case Center for Computer-Aided Engineering and Manufacturing from 1983-2002, and founded and directed MSU’s Manufacturing Research Consortium from 1993-2003. He co-founded MSU’s Genetic Algorithms Research and Applications Group (GARAGe) in 1993, conducting many projects under industrial sponsorship. In 1999, he co-founded Red Cedar Technology, Inc., (now a subsidiary of Siemens) which develops design optimization software, and was Vice President for Technology until BEACON was founded in 2010. He was chosen Michigan Distinguished Professor of the Year, 2009, by the Presidents Council, State Universities of Michigan. He was given MSU’s Distinguished Faculty Award in 2011. He was Chair of the Executive Board and a Senior Fellow of the International Society for Genetic and Evolutionary Computation, 2003-2005, and was the founding chair of ACM’s SIG on Genetic and Evolutionary Computation (SIGEVO) in 2005. He also co-leads an information and communication technology for development project in Tanzania.<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uab.academia.edu/JordiVallverdu"><img class="profile-avatar u-positionAbsolute" alt="Jordi Vallverdú" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/16298/5517/123288813/s200_jordi.vallverd_.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header 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class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Erik Goodman</h3></div><div class="js-work-strip profile--work_container" data-work-id="126783026"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783026/MOEA_D_with_Angle_based_Constrained_Dominance_Principle_for_Constrained_Multi_objective_Optimization_Problems"><img alt="Research paper thumbnail of MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/120609316/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783026/MOEA_D_with_Angle_based_Constrained_Dominance_Principle_for_Constrained_Multi_objective_Optimization_Problems">MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Feb 10, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes a novel constraint-handling mechanism named angle-based constrained dominance...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes a novel constraint-handling mechanism named angle-based constrained dominance principle (ACDP) embedded in a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multiobjective optimization problems (CMOPs). To maintain the diversity of the working population, ACDP utilizes the information of the angle of solutions to adjust the dominance relation of solutions during the evolutionary process. This paper uses 14 benchmark instances to evaluate the performance of the MOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimization problem (which is I-beam optimization problem) is optimized. The proposed MOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D, MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks and the engineering application. The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4f10d95d60857904be0051320fa09448" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609316,"asset_id":126783026,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609316/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783026"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783026"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783026; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783025"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783025/Proceedings_of_the_first_ACM_SIGEVO_Summit_on_Genetic_and_Evolutionary_Computation"><img alt="Research paper thumbnail of Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783025/Proceedings_of_the_first_ACM_SIGEVO_Summit_on_Genetic_and_Evolutionary_Computation">Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">These proceedings contain the papers presented at the 2009 World Summit on Genetic and Evolutiona...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">These proceedings contain the papers presented at the 2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit), held in Shanghai, China, June 12-14, 2009. 2009 GEC Summit is sponsored and organized by ACM SIGEVO, the Special Interest Group for Genetic and Evolutionary Computation, sponsor of the annual GECCO conferences. The 2009 GEC Summit will feature the latest research and demonstrated successes in this dynamic area, including new approaches and breakthrough applications to problems in biology, medicine, engineering design, agriculture, logistics, traffic, security, scheduling, military affairs, and other fields. It maintains an impressive record of both submission totals and acceptance rate. This year there were 372 papers submitted, with 153 accepted as full papers, giving an acceptance rate of 41%. This acceptance rate represents a healthy selection pressure in order to preserve the quality of the conference, and even though the proceedings are not bound by physical limits on the number of accepted papers, the organizers have decided to keep the acceptance rate at the lower end. The 2009 GEC Summit has adopted electronic proceedings, and we are confident that our attendees will be pleased with this format, as it greatly facilitates the keeping of all conference materials and automated searching for topics of interest. The proceedings will appear in the ACM Digital Library, making them available to the world. The 2009 GEC Summit used a strict paper review system. To reduce any bias that reviewers might have, all reviews were conducted double blind. No author&#39;s names were included in the reviewed papers. Each paper had at least 4 reviewers while each reviewer had at least 5 papers to review. About 403 researchers participated in the review process. Their work is much appreciated and is absolutely vital for the quality of the conference. In addition to the presentation of the papers contained in these proceedings, the 2009 GEC Summit also includes free tutorials. Registered participants may attend any of the 100-minute tutorials, presented by some of the world&#39;s leading experts in evolutionary computation. Tutorials are distributed throughout the three days of the conference, allowing each participant to decide, at most time slots, to attend either a tutorial or one of several sessions in which accepted papers are presented. Tutorials are grouped into introductory level, advanced, and specialized tutorials. In general, introductory tutorials in a given area will be scheduled ahead of the advanced tutorials, allowing the interested participant to attend them both in order.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783025"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783025"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783025; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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Average leaf area per leaf was more highly correlated with degreeday accumulation at a base of 4°C starting April 19, than with day of the year. Leaf area per leaf increased linearly with degree-day accumulation until full leaf expansion. Final spur or terminal leaf size was not constant between years.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4ae3bae62027a1b28cf2e6a10a9dec93" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609331,"asset_id":126783024,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609331/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783024"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783024"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783024; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783023"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783023/Genetic_Algorithm_Based_Design_of_Energy_Absorbing_Laminated_Composite_Beams"><img alt="Research paper thumbnail of Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783023/Genetic_Algorithm_Based_Design_of_Energy_Absorbing_Laminated_Composite_Beams">Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper describes a general approach to structural design using Genetic Algorithms, and an app...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper describes a general approach to structural design using Genetic Algorithms, and an application of that approach to the design of energy absorbing laminated composite beams containing distributed thin, compliant layers. We first discuss a method for applying a Genetic Algorithm (GA) to structural design, using it as an evolutionary search optimizer in conjunction with a structural simulator as its objective function. The simulator used is an efficient and robust special purpose finite element model based on a layerwise laminate theory. The GA “designs” the beam by selecting material assignments for the subregions and the locations of compliant layers, and evaluates the design using the simulator. The efficiency of the GA search is improved by use of the “injection island” architecture. The results demonstrate that the parallel GA architectures achieved algorithmic superlinear speedup to similar quality of solution in comparison with single-population genetic algorithms.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783023]").text(description); $(".js-view-count[data-work-id=126783023]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783023']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=126783023]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":126783023,"title":"Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams","internal_url":"https://www.academia.edu/126783023/Genetic_Algorithm_Based_Design_of_Energy_Absorbing_Laminated_Composite_Beams","owner_id":32766763,"coauthors_can_edit":true,"owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783022"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783022/A_concurrent_error_detection_and_correction_algorithm_for_fault_tolerant_vlsi_arithmetic_array_processors"><img alt="Research paper thumbnail of A concurrent error detection and correction algorithm for fault-tolerant vlsi arithmetic array processors" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783022/A_concurrent_error_detection_and_correction_algorithm_for_fault_tolerant_vlsi_arithmetic_array_processors">A concurrent error detection and correction algorithm for fault-tolerant vlsi arithmetic array processors</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A concurrent error detection and correction algorithm for errors caused by permanent, intermitten...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A concurrent error detection and correction algorithm for errors caused by permanent, intermittent and transient faults in arithmetic parallel-pipeline array processors, is described. The fault model, applicable to VLSI implementations, assumes the occurrence of faults with unknown origin and frequency. Recovery from faults is achieved through minimal processing element (PE) redundancy in the array architecture, facilitated by spatial bypassing of the correct operands previous to the occurrence of a fault to fault-free PE&#39;s and recomputing during the following clock cycles. The overhead hardware and timing are determined. It is shown that this concurrent error detection and correction technique uses less additional hardware than RESO (REcomputing with Shifted Operands), offers marginal timing improvement over the RESO technique, and adds the concurrent error correction capability which is not present in RESO. Furthermore, the additional hardware and control for error detection and recovery is local and modular, hence making this technique very attractive for VLSI implementations. This algorithm is based on a general fault model which assumes the occurrence of permanent, intermittent and transient fault of unknown origin and frequency. Transient and intermittent faults with a duration longer than one computational clock cycle are classified as &quot;permanent&quot;. The effects of these faults are detected as undesired changes in the logical values at the outputs of the PE&#39;s or other circuit elements, such as latches. The error detecting algorithm is concurrent and thus may detect and correct errors caused by transient faults affecting the PE&#39;s randomly.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783022"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783022; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783022]").text(description); $(".js-view-count[data-work-id=126783022]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783022; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783022']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=126783022]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":126783022,"title":"A concurrent error detection and correction algorithm for fault-tolerant vlsi arithmetic array processors","internal_url":"https://www.academia.edu/126783022/A_concurrent_error_detection_and_correction_algorithm_for_fault_tolerant_vlsi_arithmetic_array_processors","owner_id":32766763,"coauthors_can_edit":true,"owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783021"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783021/Control_allocation_based_adaptive_control_for_greenhouse_climate"><img alt="Research paper thumbnail of Control allocation-based adaptive control for greenhouse climate" class="work-thumbnail" src="https://attachments.academia-assets.com/120609332/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783021/Control_allocation_based_adaptive_control_for_greenhouse_climate">Control allocation-based adaptive control for greenhouse climate</a></div><div class="wp-workCard_item"><span>International Journal of Systems Science</span><span>, Feb 26, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents an adaptive approach to greenhouse climate control, as part of an integrated ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents an adaptive approach to greenhouse climate control, as part of an integrated control and management system for greenhouse production. In this approach, an adaptive control algorithm is first derived to guarantee the asymptotic convergence of the closed system with uncertainty, then using that control algorithm, a controller is designed to satisfy the demands for heat and mass fluxes to maintain inside temperature, humidity and CO 2 concentration at their desired values. Instead of applying the original adaptive control inputs directly, second, a control allocation technique is applied to distribute the demands of the heat and mass fluxes to the actuators by minimising tracking errors and energy consumption. To find an energy-saving solution, both single-objective optimisation (SOO) and multiobjective optimisation (MOO) in the control allocation structure are considered. The advantage of the proposed approach is that it does not require any a priori knowledge of the uncertainty bounds, and the simulation results illustrate the effectiveness of the proposed control scheme. It also indicates that MOO saves more energy in the control process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c1379c6895afd4eeb1783d3ebde07147" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609332,"asset_id":126783021,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609332/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783021"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783021"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783021; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783021]").text(description); $(".js-view-count[data-work-id=126783021]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783021; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783021']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c1379c6895afd4eeb1783d3ebde07147" } } $('.js-work-strip[data-work-id=126783021]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":126783021,"title":"Control allocation-based adaptive control for greenhouse climate","internal_url":"https://www.academia.edu/126783021/Control_allocation_based_adaptive_control_for_greenhouse_climate","owner_id":32766763,"coauthors_can_edit":true,"owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[{"id":120609332,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/120609332/thumbnails/1.jpg","file_name":"00207721.2018.144002520250104-1-fgk827.pdf","download_url":"https://www.academia.edu/attachments/120609332/download_file","bulk_download_file_name":"Control_allocation_based_adaptive_contro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/120609332/00207721.2018.144002520250104-1-fgk827-libre.pdf?1735963015=\u0026response-content-disposition=attachment%3B+filename%3DControl_allocation_based_adaptive_contro.pdf\u0026Expires=1740907278\u0026Signature=dWkSX9BT017Bu5OWAE2RGMJEjGn~Efj7A321ssbKbBWUSAR7M-x3pS8T-BN~d-bOdNIyFv3LW4DuFVoI9L-16JVrr5yoO8AJ-XlRbrMj-RPRbqs4L8F6DrfWbTa21j~j2xLWfb5JNxE54qfIrfaWpBe2M6vw89vgRLR9nTbrgXyMCLZqkeA3N9BdrVgV950Ckbt9DkLVqMKYgKdbBDT5khQhtvorXPftgD25ZUa-aKL6Ys23ZaEa9HYiv4UVVfm7XR2QqXvEr5rq-lpKJf7z6Q5s~XqGkLPjjH35VavYSWZqp8GHxDcK~NuRN-GLlXscB-e0raU7U-T4WNPVConFPA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783020"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783020/A_hands_on_paradigm_for_EAP_education_undergraduates_pre_college_students_and_beyond"><img alt="Research paper thumbnail of A hands-on paradigm for EAP education: undergraduates, pre-college students, and beyond" class="work-thumbnail" src="https://attachments.academia-assets.com/120609333/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783020/A_hands_on_paradigm_for_EAP_education_undergraduates_pre_college_students_and_beyond">A hands-on paradigm for EAP education: undergraduates, pre-college students, and beyond</a></div><div class="wp-workCard_item"><span>Proceedings of SPIE</span><span>, Apr 6, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Electroactive polymers (EAPs) are receiving increasing interest from researchers due to their uni...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Electroactive polymers (EAPs) are receiving increasing interest from researchers due to their unique capabilities and numerous potential applications in biomimetic robots, smart structures, biomedical devices, and micro/nanomanipulation. Since these materials are relatively new, it is imperative to educate students and the general public to raise their awareness of EAP potentials and produce the talent pool needed for continuing, rapid advances in the field of EAPs. In this paper we describe our concerted effort in teaching EAP to undergraduates, grade school students, and the general public, through hands-on research and learning on EAP-based biomimetic robots. Two integrated activities are highlighted: A senior Capstone design program on EAP robots, and the subsequent programs that use these developed robots to reach out to pre-college students. A robotic fish and a sociable robot enabled by ionic polymer-metal composite materials are used as examples throughout the paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="47dda67b70f809fbdceb23057e1f80e7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609333,"asset_id":126783020,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609333/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783020"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783020"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783020; 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</script> <div class="js-work-strip profile--work_container" data-work-id="126783018"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783018/A_collaboration_based_particle_swarm_optimizer_with_history_guided_estimation_for_optimization_in_dynamic_environments"><img alt="Research paper thumbnail of A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments" class="work-thumbnail" src="https://attachments.academia-assets.com/120609330/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783018/A_collaboration_based_particle_swarm_optimizer_with_history_guided_estimation_for_optimization_in_dynamic_environments">A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments</a></div><div class="wp-workCard_item"><span>Expert Systems With Applications</span><span>, Apr 1, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper introduces a collaboration-based particle swarm optimizer (PSO) by incorporating three...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper introduces a collaboration-based particle swarm optimizer (PSO) by incorporating three new strategies: a global learning strategy, a probability of learning, and a "worst replacement" swarm update rule. Instead of learning from the personal historical best position and the global (or local) best position which was used by the classical PSO, a target particle learns from another randomly chosen particle and the global best one in the swarm. Instead of accepting a new velocity directly, the velocity updates according to a learning probability, according to which the velocity of the target particle in each dimension updates via learning from other particles or simply inherits its previous velocity component. Since each particle has the same chance to be selected as a leader, the worst particle might influence the whole swarm's performance. Therefore, the worst particle in the swarm in each update is moved to a new better position generated from another particle. The proposed algorithm is shown to be statistically significantly better than six other state-of-the-art PSO variants on 20 typical benchmark functions with three different dimensionalities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="002dc7f83e660be3d4af87cbd51e47ff" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609330,"asset_id":126783018,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609330/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783018"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783018"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783018; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783017"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783017/Nearly_dynamic_programming_NN_approximation_based_optimal_control_for_greenhouse_climate_A_simulation_study"><img alt="Research paper thumbnail of Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study" class="work-thumbnail" src="https://attachments.academia-assets.com/120609329/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783017/Nearly_dynamic_programming_NN_approximation_based_optimal_control_for_greenhouse_climate_A_simulation_study">Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study</a></div><div class="wp-workCard_item"><span>Optimal Control Applications & Methods</span><span>, Oct 12, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Greenhouse production must create a suitable growth environment for its crop to improve yield whi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Greenhouse production must create a suitable growth environment for its crop to improve yield while minimizing the energy used to maintain the greenhouse environment, thereby reducing production cost. To this end, this paper develops a nearly optimal control approach based on adaptive dynamic programming .In this method, three neural networks(NN) are used to estimate the value function and control policy, and to compensate for the unmodeled dynamics of the greenhouse climate. Taking into account the greenhouse system typically being an over-actuated system, the total control efforts for heat, fog and CO 2 are considered as the virtual control inputs to be generated by the optimal controller. To obtain the real control inputs, a control allocation technique is introduced to distribute the virtual control inputs to the actuators. Finally, Lyapunov stability analysis is performed to derive the update law of the NNs to ensure the asymptotic convergence of the closed-loop system, and simulation is carried out to illustrate the effectiveness and control performance of the proposed approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b88b0de11faa12760051db3c4584b687" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609329,"asset_id":126783017,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609329/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783017"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783017"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783017; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783016"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783016/Optimal_design_of_laminated_composite_structures_using_coarse_grain_parallel_genetic_algorithms"><img alt="Research paper thumbnail of Optimal design of laminated composite structures using coarse-grain parallel genetic algorithms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783016/Optimal_design_of_laminated_composite_structures_using_coarse_grain_parallel_genetic_algorithms">Optimal design of laminated composite structures using coarse-grain parallel genetic algorithms</a></div><div class="wp-workCard_item"><span>Computing Systems in Engineering</span><span>, Aug 1, 1994</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new coarse-grain parallel architecture for genetic algorithms, called island injection genetic ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new coarse-grain parallel architecture for genetic algorithms, called island injection genetic algorithms, is implemented for the optimal design of laminated composite structures. This approach represents the design at various levels of refinement in subpopulations on separate ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783016"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783016"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783016; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783016]").text(description); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783015]").text(description); $(".js-view-count[data-work-id=126783015]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783015; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783015']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783014"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783014/A_neighbor_based_learning_particle_swarm_optimizer_with_short_term_and_long_term_memory_for_dynamic_optimization_problems"><img alt="Research paper thumbnail of A neighbor-based learning particle swarm optimizer with short-term and long-term memory for dynamic optimization problems" class="work-thumbnail" src="https://attachments.academia-assets.com/120609327/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783014/A_neighbor_based_learning_particle_swarm_optimizer_with_short_term_and_long_term_memory_for_dynamic_optimization_problems">A neighbor-based learning particle swarm optimizer with short-term and long-term memory for dynamic optimization problems</a></div><div class="wp-workCard_item"><span>Information Sciences</span><span>, Jul 1, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a novel Particle Swarm Optimization algorithm to address Dynamic Optimization...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a novel Particle Swarm Optimization algorithm to address Dynamic Optimization Problems. The algorithm incorporates a neighbor-based learning strategy into the velocity update of Particle Swarm Optimization, in order to enhance the exploration and exploitation capabilities of particles. Unlike the traditional swarm update scheme, a "worst replacement" strategy is used to update the swarm, whereby the position of the worst particle in the swarm is replaced by a better newly generated position. The shortterm memory is employed to store solutions with intermediate fitnesses from the most recent environment, and the long-term memory is to store the historical best solutions found in all previous environments. After an environmental change is detected, some particles' positions in the swarm are replaced by the members of the short-term memory, and the best member in the long-term memory under the current environment is re-introduced to the active swarm along with its Gaussian neighborhood, then the remaining particles' positions are re-initialized. The performance of the proposed algorithm is compared with six state-of-the-art dynamic algorithms over the Moving Peaks Benchmark problems and Dynamic Rotation Peak Benchmark Generator. Experimental results indicate that out algorithm obtains superior performance compared with the competitors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6902e32ecbc9c71e37957e19898e899" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609327,"asset_id":126783014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609327/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783014; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783013"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783013/Decomposition_based_evolutionary_dynamic_multiobjective_optimization_using_a_difference_model"><img alt="Research paper thumbnail of Decomposition-based evolutionary dynamic multiobjective optimization using a difference model" class="work-thumbnail" src="https://attachments.academia-assets.com/120609325/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783013/Decomposition_based_evolutionary_dynamic_multiobjective_optimization_using_a_difference_model">Decomposition-based evolutionary dynamic multiobjective optimization using a difference model</a></div><div class="wp-workCard_item"><span>Applied Soft Computing</span><span>, Mar 1, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a novel prediction model combined with a multiobjective evolutionary algorith...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a novel prediction model combined with a multiobjective evolutionary algorithm based on decomposition to solve dynamic multiobjective optimization problems. In our model, the motion of approximated Pareto-optimal solutions (POS) over time is represented by the motion of the centroid, and the other solutions are assumed to have the same motion as the centroid. A history of recent centroid locations is used to build a difference model to estimate the later motion of the centroid when an environmental change is detected, and then the new locations of the other solutions are predicted based on their current locations and the estimated motion. The predicted solutions, combined with some retained solutions, form a new population to explore the new environment, and are expected to track the new POS and/or Pareto-optimal front relatively well. The proposed algorithm is compared with four state-of-the-art dynamic multiobjective evolutionary algorithms through 20 benchmark problems with differing dynamic characteristics. The experimental studies show that the proposed algorithm is effective in dealing with dynamic problems and clearly outperforms the competitors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea1193726c7356011c278fed284ff851" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609325,"asset_id":126783013,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609325/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783013"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783013; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783010"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783010/An_Improved_Epsilon_Constraint_handling_Method_in_MOEA_D_for_CMOPs_with_Large_Infeasible_Regions"><img alt="Research paper thumbnail of An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions" class="work-thumbnail" src="https://attachments.academia-assets.com/120609314/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783010/An_Improved_Epsilon_Constraint_handling_Method_in_MOEA_D_for_CMOPs_with_Large_Infeasible_Regions">An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jul 27, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a dec...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then the fourteen benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="39b375579905679d6f5b3311ad578c8c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609314,"asset_id":126783010,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609314/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783010"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783010"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783010; 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</script> <div class="js-work-strip profile--work_container" data-work-id="126783007"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783007/A_Computer_Simulation_Model_for_Population_Studies_of_Woodland_Pool_Aedes_Mosquitoes1"><img alt="Research paper thumbnail of A Computer Simulation Model for Population Studies of Woodland Pool Aedes Mosquitoes1" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783007/A_Computer_Simulation_Model_for_Population_Studies_of_Woodland_Pool_Aedes_Mosquitoes1">A Computer Simulation Model for Population Studies of Woodland Pool Aedes Mosquitoes1</a></div><div class="wp-workCard_item"><span>Environmental Entomology</span><span>, Dec 1, 1975</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A system model for woodland pool Aedes was constructed and a computer simulation implementing the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A system model for woodland pool Aedes was constructed and a computer simulation implementing the system model was constructed and refined for populations of A. stimulans-fitchii mosquitoes. Information and data utilized in the model construction were obtained from surveys conducted in Michigan state parks and an extensive literature review. Dynamics of the mosquito&#39;s life cycle and of the woodland pool ecosystem were modeled and the resultant equations are described. Assumptions used in the modeling process are briefly discussed in order to show the reasoning process underlying the use of these equations in the simulation. A description of computer program structure and data files utilized by the program is given. Research on and refinement of the physical components consisted of measurements during 1973 on aquatic parameters chosen as key factors in immature mosquito dynamics and meteorological features oriented as stimuli to these parameters. The resultant data were statistically analyzed to obtain the best available model for maximum water temperature, dissolved oxygen concentration and water depth.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783007"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783007"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783007; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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To maintain the diversity of the working population, ACDP utilizes the information of the angle of solutions to adjust the dominance relation of solutions during the evolutionary process. This paper uses 14 benchmark instances to evaluate the performance of the MOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimization problem (which is I-beam optimization problem) is optimized. The proposed MOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D, MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks and the engineering application. The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4f10d95d60857904be0051320fa09448" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609316,"asset_id":126783026,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609316/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783026"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783026"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783026; 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The 2009 GEC Summit will feature the latest research and demonstrated successes in this dynamic area, including new approaches and breakthrough applications to problems in biology, medicine, engineering design, agriculture, logistics, traffic, security, scheduling, military affairs, and other fields. It maintains an impressive record of both submission totals and acceptance rate. This year there were 372 papers submitted, with 153 accepted as full papers, giving an acceptance rate of 41%. This acceptance rate represents a healthy selection pressure in order to preserve the quality of the conference, and even though the proceedings are not bound by physical limits on the number of accepted papers, the organizers have decided to keep the acceptance rate at the lower end. The 2009 GEC Summit has adopted electronic proceedings, and we are confident that our attendees will be pleased with this format, as it greatly facilitates the keeping of all conference materials and automated searching for topics of interest. The proceedings will appear in the ACM Digital Library, making them available to the world. The 2009 GEC Summit used a strict paper review system. To reduce any bias that reviewers might have, all reviews were conducted double blind. No author&#39;s names were included in the reviewed papers. Each paper had at least 4 reviewers while each reviewer had at least 5 papers to review. About 403 researchers participated in the review process. Their work is much appreciated and is absolutely vital for the quality of the conference. In addition to the presentation of the papers contained in these proceedings, the 2009 GEC Summit also includes free tutorials. Registered participants may attend any of the 100-minute tutorials, presented by some of the world&#39;s leading experts in evolutionary computation. Tutorials are distributed throughout the three days of the conference, allowing each participant to decide, at most time slots, to attend either a tutorial or one of several sessions in which accepted papers are presented. Tutorials are grouped into introductory level, advanced, and specialized tutorials. In general, introductory tutorials in a given area will be scheduled ahead of the advanced tutorials, allowing the interested participant to attend them both in order.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783025"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783025"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783025; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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Average leaf area per leaf was more highly correlated with degreeday accumulation at a base of 4°C starting April 19, than with day of the year. Leaf area per leaf increased linearly with degree-day accumulation until full leaf expansion. Final spur or terminal leaf size was not constant between years.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4ae3bae62027a1b28cf2e6a10a9dec93" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609331,"asset_id":126783024,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609331/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783024"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783024"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783024; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783023"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783023/Genetic_Algorithm_Based_Design_of_Energy_Absorbing_Laminated_Composite_Beams"><img alt="Research paper thumbnail of Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783023/Genetic_Algorithm_Based_Design_of_Energy_Absorbing_Laminated_Composite_Beams">Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper describes a general approach to structural design using Genetic Algorithms, and an app...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper describes a general approach to structural design using Genetic Algorithms, and an application of that approach to the design of energy absorbing laminated composite beams containing distributed thin, compliant layers. We first discuss a method for applying a Genetic Algorithm (GA) to structural design, using it as an evolutionary search optimizer in conjunction with a structural simulator as its objective function. The simulator used is an efficient and robust special purpose finite element model based on a layerwise laminate theory. The GA “designs” the beam by selecting material assignments for the subregions and the locations of compliant layers, and evaluates the design using the simulator. The efficiency of the GA search is improved by use of the “injection island” architecture. The results demonstrate that the parallel GA architectures achieved algorithmic superlinear speedup to similar quality of solution in comparison with single-population genetic algorithms.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783023]").text(description); $(".js-view-count[data-work-id=126783023]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783023']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=126783023]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":126783023,"title":"Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams","internal_url":"https://www.academia.edu/126783023/Genetic_Algorithm_Based_Design_of_Energy_Absorbing_Laminated_Composite_Beams","owner_id":32766763,"coauthors_can_edit":true,"owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783022"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783022/A_concurrent_error_detection_and_correction_algorithm_for_fault_tolerant_vlsi_arithmetic_array_processors"><img alt="Research paper thumbnail of A concurrent error detection and correction algorithm for fault-tolerant vlsi arithmetic array processors" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783022/A_concurrent_error_detection_and_correction_algorithm_for_fault_tolerant_vlsi_arithmetic_array_processors">A concurrent error detection and correction algorithm for fault-tolerant vlsi arithmetic array processors</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A concurrent error detection and correction algorithm for errors caused by permanent, intermitten...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A concurrent error detection and correction algorithm for errors caused by permanent, intermittent and transient faults in arithmetic parallel-pipeline array processors, is described. The fault model, applicable to VLSI implementations, assumes the occurrence of faults with unknown origin and frequency. Recovery from faults is achieved through minimal processing element (PE) redundancy in the array architecture, facilitated by spatial bypassing of the correct operands previous to the occurrence of a fault to fault-free PE&#39;s and recomputing during the following clock cycles. The overhead hardware and timing are determined. It is shown that this concurrent error detection and correction technique uses less additional hardware than RESO (REcomputing with Shifted Operands), offers marginal timing improvement over the RESO technique, and adds the concurrent error correction capability which is not present in RESO. Furthermore, the additional hardware and control for error detection and recovery is local and modular, hence making this technique very attractive for VLSI implementations. This algorithm is based on a general fault model which assumes the occurrence of permanent, intermittent and transient fault of unknown origin and frequency. Transient and intermittent faults with a duration longer than one computational clock cycle are classified as &quot;permanent&quot;. The effects of these faults are detected as undesired changes in the logical values at the outputs of the PE&#39;s or other circuit elements, such as latches. The error detecting algorithm is concurrent and thus may detect and correct errors caused by transient faults affecting the PE&#39;s randomly.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783022"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783022; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783022]").text(description); $(".js-view-count[data-work-id=126783022]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783022; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783022']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=126783022]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":126783022,"title":"A concurrent error detection and correction algorithm for fault-tolerant vlsi arithmetic array processors","internal_url":"https://www.academia.edu/126783022/A_concurrent_error_detection_and_correction_algorithm_for_fault_tolerant_vlsi_arithmetic_array_processors","owner_id":32766763,"coauthors_can_edit":true,"owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783021"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783021/Control_allocation_based_adaptive_control_for_greenhouse_climate"><img alt="Research paper thumbnail of Control allocation-based adaptive control for greenhouse climate" class="work-thumbnail" src="https://attachments.academia-assets.com/120609332/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783021/Control_allocation_based_adaptive_control_for_greenhouse_climate">Control allocation-based adaptive control for greenhouse climate</a></div><div class="wp-workCard_item"><span>International Journal of Systems Science</span><span>, Feb 26, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents an adaptive approach to greenhouse climate control, as part of an integrated ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents an adaptive approach to greenhouse climate control, as part of an integrated control and management system for greenhouse production. In this approach, an adaptive control algorithm is first derived to guarantee the asymptotic convergence of the closed system with uncertainty, then using that control algorithm, a controller is designed to satisfy the demands for heat and mass fluxes to maintain inside temperature, humidity and CO 2 concentration at their desired values. Instead of applying the original adaptive control inputs directly, second, a control allocation technique is applied to distribute the demands of the heat and mass fluxes to the actuators by minimising tracking errors and energy consumption. To find an energy-saving solution, both single-objective optimisation (SOO) and multiobjective optimisation (MOO) in the control allocation structure are considered. The advantage of the proposed approach is that it does not require any a priori knowledge of the uncertainty bounds, and the simulation results illustrate the effectiveness of the proposed control scheme. It also indicates that MOO saves more energy in the control process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c1379c6895afd4eeb1783d3ebde07147" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609332,"asset_id":126783021,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609332/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783021"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783021"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783021; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783021]").text(description); $(".js-view-count[data-work-id=126783021]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783021; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783021']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c1379c6895afd4eeb1783d3ebde07147" } } $('.js-work-strip[data-work-id=126783021]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":126783021,"title":"Control allocation-based adaptive control for greenhouse climate","internal_url":"https://www.academia.edu/126783021/Control_allocation_based_adaptive_control_for_greenhouse_climate","owner_id":32766763,"coauthors_can_edit":true,"owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[{"id":120609332,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/120609332/thumbnails/1.jpg","file_name":"00207721.2018.144002520250104-1-fgk827.pdf","download_url":"https://www.academia.edu/attachments/120609332/download_file","bulk_download_file_name":"Control_allocation_based_adaptive_contro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/120609332/00207721.2018.144002520250104-1-fgk827-libre.pdf?1735963015=\u0026response-content-disposition=attachment%3B+filename%3DControl_allocation_based_adaptive_contro.pdf\u0026Expires=1740907278\u0026Signature=dWkSX9BT017Bu5OWAE2RGMJEjGn~Efj7A321ssbKbBWUSAR7M-x3pS8T-BN~d-bOdNIyFv3LW4DuFVoI9L-16JVrr5yoO8AJ-XlRbrMj-RPRbqs4L8F6DrfWbTa21j~j2xLWfb5JNxE54qfIrfaWpBe2M6vw89vgRLR9nTbrgXyMCLZqkeA3N9BdrVgV950Ckbt9DkLVqMKYgKdbBDT5khQhtvorXPftgD25ZUa-aKL6Ys23ZaEa9HYiv4UVVfm7XR2QqXvEr5rq-lpKJf7z6Q5s~XqGkLPjjH35VavYSWZqp8GHxDcK~NuRN-GLlXscB-e0raU7U-T4WNPVConFPA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783020"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783020/A_hands_on_paradigm_for_EAP_education_undergraduates_pre_college_students_and_beyond"><img alt="Research paper thumbnail of A hands-on paradigm for EAP education: undergraduates, pre-college students, and beyond" class="work-thumbnail" src="https://attachments.academia-assets.com/120609333/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783020/A_hands_on_paradigm_for_EAP_education_undergraduates_pre_college_students_and_beyond">A hands-on paradigm for EAP education: undergraduates, pre-college students, and beyond</a></div><div class="wp-workCard_item"><span>Proceedings of SPIE</span><span>, Apr 6, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Electroactive polymers (EAPs) are receiving increasing interest from researchers due to their uni...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Electroactive polymers (EAPs) are receiving increasing interest from researchers due to their unique capabilities and numerous potential applications in biomimetic robots, smart structures, biomedical devices, and micro/nanomanipulation. Since these materials are relatively new, it is imperative to educate students and the general public to raise their awareness of EAP potentials and produce the talent pool needed for continuing, rapid advances in the field of EAPs. In this paper we describe our concerted effort in teaching EAP to undergraduates, grade school students, and the general public, through hands-on research and learning on EAP-based biomimetic robots. Two integrated activities are highlighted: A senior Capstone design program on EAP robots, and the subsequent programs that use these developed robots to reach out to pre-college students. A robotic fish and a sociable robot enabled by ionic polymer-metal composite materials are used as examples throughout the paper.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="47dda67b70f809fbdceb23057e1f80e7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609333,"asset_id":126783020,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609333/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783020"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783020"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783020; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783019"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783019/Multi_layer_hierarchical_optimisation_of_greenhouse_climate_setpoints_for_energy_conservation_and_improvement_of_crop_yield"><img alt="Research paper thumbnail of Multi-layer hierarchical optimisation of greenhouse climate setpoints for energy conservation and improvement of crop yield" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783019/Multi_layer_hierarchical_optimisation_of_greenhouse_climate_setpoints_for_energy_conservation_and_improvement_of_crop_yield">Multi-layer hierarchical optimisation of greenhouse climate setpoints for energy conservation and improvement of crop yield</a></div><div class="wp-workCard_item"><span>Biosystems Engineering</span><span>, May 1, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783019"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783019"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783019; 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</script> <div class="js-work-strip profile--work_container" data-work-id="126783018"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783018/A_collaboration_based_particle_swarm_optimizer_with_history_guided_estimation_for_optimization_in_dynamic_environments"><img alt="Research paper thumbnail of A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments" class="work-thumbnail" src="https://attachments.academia-assets.com/120609330/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783018/A_collaboration_based_particle_swarm_optimizer_with_history_guided_estimation_for_optimization_in_dynamic_environments">A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments</a></div><div class="wp-workCard_item"><span>Expert Systems With Applications</span><span>, Apr 1, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper introduces a collaboration-based particle swarm optimizer (PSO) by incorporating three...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper introduces a collaboration-based particle swarm optimizer (PSO) by incorporating three new strategies: a global learning strategy, a probability of learning, and a "worst replacement" swarm update rule. Instead of learning from the personal historical best position and the global (or local) best position which was used by the classical PSO, a target particle learns from another randomly chosen particle and the global best one in the swarm. Instead of accepting a new velocity directly, the velocity updates according to a learning probability, according to which the velocity of the target particle in each dimension updates via learning from other particles or simply inherits its previous velocity component. Since each particle has the same chance to be selected as a leader, the worst particle might influence the whole swarm's performance. Therefore, the worst particle in the swarm in each update is moved to a new better position generated from another particle. The proposed algorithm is shown to be statistically significantly better than six other state-of-the-art PSO variants on 20 typical benchmark functions with three different dimensionalities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="002dc7f83e660be3d4af87cbd51e47ff" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609330,"asset_id":126783018,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609330/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783018"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783018"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783018; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783018]").text(description); $(".js-view-count[data-work-id=126783018]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 126783018; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='126783018']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783017"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783017/Nearly_dynamic_programming_NN_approximation_based_optimal_control_for_greenhouse_climate_A_simulation_study"><img alt="Research paper thumbnail of Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study" class="work-thumbnail" src="https://attachments.academia-assets.com/120609329/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783017/Nearly_dynamic_programming_NN_approximation_based_optimal_control_for_greenhouse_climate_A_simulation_study">Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study</a></div><div class="wp-workCard_item"><span>Optimal Control Applications & Methods</span><span>, Oct 12, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Greenhouse production must create a suitable growth environment for its crop to improve yield whi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Greenhouse production must create a suitable growth environment for its crop to improve yield while minimizing the energy used to maintain the greenhouse environment, thereby reducing production cost. To this end, this paper develops a nearly optimal control approach based on adaptive dynamic programming .In this method, three neural networks(NN) are used to estimate the value function and control policy, and to compensate for the unmodeled dynamics of the greenhouse climate. Taking into account the greenhouse system typically being an over-actuated system, the total control efforts for heat, fog and CO 2 are considered as the virtual control inputs to be generated by the optimal controller. To obtain the real control inputs, a control allocation technique is introduced to distribute the virtual control inputs to the actuators. Finally, Lyapunov stability analysis is performed to derive the update law of the NNs to ensure the asymptotic convergence of the closed-loop system, and simulation is carried out to illustrate the effectiveness and control performance of the proposed approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b88b0de11faa12760051db3c4584b687" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609329,"asset_id":126783017,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609329/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783017"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783017"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783017; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783016"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783016/Optimal_design_of_laminated_composite_structures_using_coarse_grain_parallel_genetic_algorithms"><img alt="Research paper thumbnail of Optimal design of laminated composite structures using coarse-grain parallel genetic algorithms" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783016/Optimal_design_of_laminated_composite_structures_using_coarse_grain_parallel_genetic_algorithms">Optimal design of laminated composite structures using coarse-grain parallel genetic algorithms</a></div><div class="wp-workCard_item"><span>Computing Systems in Engineering</span><span>, Aug 1, 1994</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new coarse-grain parallel architecture for genetic algorithms, called island injection genetic ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new coarse-grain parallel architecture for genetic algorithms, called island injection genetic algorithms, is implemented for the optimal design of laminated composite structures. This approach represents the design at various levels of refinement in subpopulations on separate ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783016"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783016"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783016; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=126783016]").text(description); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783014"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783014/A_neighbor_based_learning_particle_swarm_optimizer_with_short_term_and_long_term_memory_for_dynamic_optimization_problems"><img alt="Research paper thumbnail of A neighbor-based learning particle swarm optimizer with short-term and long-term memory for dynamic optimization problems" class="work-thumbnail" src="https://attachments.academia-assets.com/120609327/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783014/A_neighbor_based_learning_particle_swarm_optimizer_with_short_term_and_long_term_memory_for_dynamic_optimization_problems">A neighbor-based learning particle swarm optimizer with short-term and long-term memory for dynamic optimization problems</a></div><div class="wp-workCard_item"><span>Information Sciences</span><span>, Jul 1, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a novel Particle Swarm Optimization algorithm to address Dynamic Optimization...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a novel Particle Swarm Optimization algorithm to address Dynamic Optimization Problems. The algorithm incorporates a neighbor-based learning strategy into the velocity update of Particle Swarm Optimization, in order to enhance the exploration and exploitation capabilities of particles. Unlike the traditional swarm update scheme, a "worst replacement" strategy is used to update the swarm, whereby the position of the worst particle in the swarm is replaced by a better newly generated position. The shortterm memory is employed to store solutions with intermediate fitnesses from the most recent environment, and the long-term memory is to store the historical best solutions found in all previous environments. After an environmental change is detected, some particles' positions in the swarm are replaced by the members of the short-term memory, and the best member in the long-term memory under the current environment is re-introduced to the active swarm along with its Gaussian neighborhood, then the remaining particles' positions are re-initialized. The performance of the proposed algorithm is compared with six state-of-the-art dynamic algorithms over the Moving Peaks Benchmark problems and Dynamic Rotation Peak Benchmark Generator. Experimental results indicate that out algorithm obtains superior performance compared with the competitors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6902e32ecbc9c71e37957e19898e899" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609327,"asset_id":126783014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609327/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783014; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="126783013"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/126783013/Decomposition_based_evolutionary_dynamic_multiobjective_optimization_using_a_difference_model"><img alt="Research paper thumbnail of Decomposition-based evolutionary dynamic multiobjective optimization using a difference model" class="work-thumbnail" src="https://attachments.academia-assets.com/120609325/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/126783013/Decomposition_based_evolutionary_dynamic_multiobjective_optimization_using_a_difference_model">Decomposition-based evolutionary dynamic multiobjective optimization using a difference model</a></div><div class="wp-workCard_item"><span>Applied Soft Computing</span><span>, Mar 1, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a novel prediction model combined with a multiobjective evolutionary algorith...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a novel prediction model combined with a multiobjective evolutionary algorithm based on decomposition to solve dynamic multiobjective optimization problems. In our model, the motion of approximated Pareto-optimal solutions (POS) over time is represented by the motion of the centroid, and the other solutions are assumed to have the same motion as the centroid. A history of recent centroid locations is used to build a difference model to estimate the later motion of the centroid when an environmental change is detected, and then the new locations of the other solutions are predicted based on their current locations and the estimated motion. The predicted solutions, combined with some retained solutions, form a new population to explore the new environment, and are expected to track the new POS and/or Pareto-optimal front relatively well. The proposed algorithm is compared with four state-of-the-art dynamic multiobjective evolutionary algorithms through 20 benchmark problems with differing dynamic characteristics. The experimental studies show that the proposed algorithm is effective in dealing with dynamic problems and clearly outperforms the competitors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea1193726c7356011c278fed284ff851" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609325,"asset_id":126783013,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609325/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783013"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783013; 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The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible regions (relative to the feasible regions), and they are called LIR-CMOPs. Then the fourteen benchmarks, including LIR-CMOP1-14, are used to test MOEA/D-IEpsilon and four other decomposition-based CMOEAs, including MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP and C-MOEA/D. The experimental results indicate that MOEA/D-IEpsilon is significantly better than the other four CMOEAs on all of the test instances, which shows that MOEA/D-IEpsilon is more suitable for solving CMOPs with large infeasible regions. Furthermore, a real-world problem, namely the robot gripper optimization problem, is used to test the five CMOEAs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="39b375579905679d6f5b3311ad578c8c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120609314,"asset_id":126783010,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120609314/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="126783010"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126783010"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126783010; 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</script> <div class="js-work-strip profile--work_container" data-work-id="126783007"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/126783007/A_Computer_Simulation_Model_for_Population_Studies_of_Woodland_Pool_Aedes_Mosquitoes1"><img alt="Research paper thumbnail of A Computer Simulation Model for Population Studies of Woodland Pool Aedes Mosquitoes1" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/126783007/A_Computer_Simulation_Model_for_Population_Studies_of_Woodland_Pool_Aedes_Mosquitoes1">A Computer Simulation Model for Population Studies of Woodland Pool Aedes Mosquitoes1</a></div><div class="wp-workCard_item"><span>Environmental Entomology</span><span>, Dec 1, 1975</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A system model for woodland pool Aedes was constructed and a computer simulation implementing the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A system model for woodland pool Aedes was constructed and a computer simulation implementing the system model was constructed and refined for populations of A. stimulans-fitchii mosquitoes. Information and data utilized in the model construction were obtained from surveys conducted in Michigan state parks and an extensive literature review. Dynamics of the mosquito&#39;s life cycle and of the woodland pool ecosystem were modeled and the resultant equations are described. Assumptions used in the modeling process are briefly discussed in order to show the reasoning process underlying the use of these equations in the simulation. A description of computer program structure and data files utilized by the program is given. Research on and refinement of the physical components consisted of measurements during 1973 on aquatic parameters chosen as key factors in immature mosquito dynamics and meteorological features oriented as stimuli to these parameters. 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