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optimization","created_at":"2015-10-06T11:54:50.169-07:00","url":"https://www.academia.edu/16517307/An_evolutionary_algorithm_based_approach_to_robust_analog_circuit_design_using_constrained_multi_objective_optimization?f_ri=143163","dom_id":"work_16517307","summary":null,"downloadable_attachments":[{"id":42450634,"asset_id":16517307,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35764061,"first_name":"Giuseppe","last_name":"Nicosia","domain_name":"cambridge","page_name":"GiuseppeNicosia","display_name":"Giuseppe Nicosia","profile_url":"https://cambridge.academia.edu/GiuseppeNicosia?f_ri=143163","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":979,"name":"Analog Circuits","url":"https://www.academia.edu/Documents/in/Analog_Circuits?f_ri=143163","nofollow":false},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=143163","nofollow":false},{"id":30329,"name":"Genetic 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Manufacture","url":"https://www.academia.edu/Documents/in/Design_for_Manufacture?f_ri=143163"},{"id":555139,"name":"Evolutionary optimization","url":"https://www.academia.edu/Documents/in/Evolutionary_optimization?f_ri=143163"},{"id":570997,"name":"Pareto front","url":"https://www.academia.edu/Documents/in/Pareto_front?f_ri=143163"},{"id":648356,"name":"Integrated Circuit","url":"https://www.academia.edu/Documents/in/Integrated_Circuit?f_ri=143163"},{"id":681868,"name":"Analog Integrated Circuits","url":"https://www.academia.edu/Documents/in/Analog_Integrated_Circuits?f_ri=143163"},{"id":1146982,"name":"Ultra Wideband","url":"https://www.academia.edu/Documents/in/Ultra_Wideband?f_ri=143163"},{"id":1548911,"name":"Test Bed","url":"https://www.academia.edu/Documents/in/Test_Bed?f_ri=143163"},{"id":1689234,"name":"Constrained Optimization","url":"https://www.academia.edu/Documents/in/Constrained_Optimization?f_ri=143163"},{"id":1935769,"name":"Design Model","url":"https://www.academia.edu/Documents/in/Design_Model?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_79705730" data-work_id="79705730" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/79705730/Planned_Scheduling_for_Economic_Power_Sharing_in_a_CHP_Based_Micro_Grid">Planned Scheduling for Economic Power Sharing in a CHP-Based Micro-Grid</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79705730" data-share-source="work_strip" 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itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="48621982" href="https://independent.academia.edu/DrAniruddhaBhattacharya">DrAniruddha Bhattacharya</a><script data-card-contents-for-user="48621982" type="text/json">{"id":48621982,"first_name":"DrAniruddha","last_name":"Bhattacharya","domain_name":"independent","page_name":"DrAniruddhaBhattacharya","display_name":"DrAniruddha Bhattacharya","profile_url":"https://independent.academia.edu/DrAniruddhaBhattacharya?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_79705730 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="79705730"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 79705730, container: ".js-paper-rank-work_79705730", }); 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type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1424" href="https://www.academia.edu/Documents/in/Power_Systems">Power Systems</a>, <script data-card-contents-for-ri="1424" type="text/json">{"id":1424,"name":"Power Systems","url":"https://www.academia.edu/Documents/in/Power_Systems?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="12346" href="https://www.academia.edu/Documents/in/Differential_Evolution">Differential Evolution</a>, <script data-card-contents-for-ri="12346" type="text/json">{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a><script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=79705730]'), work: {"id":79705730,"title":"Planned Scheduling for Economic Power Sharing in a CHP-Based Micro-Grid","created_at":"2022-05-22T23:40:45.092-07:00","url":"https://www.academia.edu/79705730/Planned_Scheduling_for_Economic_Power_Sharing_in_a_CHP_Based_Micro_Grid?f_ri=143163","dom_id":"work_79705730","summary":null,"downloadable_attachments":[{"id":86329751,"asset_id":79705730,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":48621982,"first_name":"DrAniruddha","last_name":"Bhattacharya","domain_name":"independent","page_name":"DrAniruddhaBhattacharya","display_name":"DrAniruddha Bhattacharya","profile_url":"https://independent.academia.edu/DrAniruddhaBhattacharya?f_ri=143163","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=143163","nofollow":false},{"id":1424,"name":"Power Systems","url":"https://www.academia.edu/Documents/in/Power_Systems?f_ri=143163","nofollow":false},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution?f_ri=143163","nofollow":false},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false},{"id":279849,"name":"Density Estimation","url":"https://www.academia.edu/Documents/in/Density_Estimation?f_ri=143163"},{"id":584683,"name":"Waste minimisation","url":"https://www.academia.edu/Documents/in/Waste_minimisation?f_ri=143163"},{"id":679783,"name":"Boolean Satisfiability","url":"https://www.academia.edu/Documents/in/Boolean_Satisfiability?f_ri=143163"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=143163"},{"id":1272117,"name":"Combined Heat and Power","url":"https://www.academia.edu/Documents/in/Combined_Heat_and_Power?f_ri=143163"},{"id":1330964,"name":"Particle Swarm Optimizer","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimizer?f_ri=143163"},{"id":2124420,"name":"Optimal location","url":"https://www.academia.edu/Documents/in/Optimal_location?f_ri=143163"},{"id":3311597,"name":"Heat Balance","url":"https://www.academia.edu/Documents/in/Heat_Balance?f_ri=143163"},{"id":4085481,"name":"inequality constraint","url":"https://www.academia.edu/Documents/in/inequality_constraint?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_71273197" data-work_id="71273197" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/71273197/Control_of_a_Reverse_Osmosis_plant_by_using_a_robust_PID_design_based_on_multi_objective_optimization">Control of a Reverse Osmosis plant by using a robust PID design based on multi-objective optimization</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/71273197" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5a2e13a24c8ef1bf0b3cb3a80a229ed8" rel="nofollow" data-download="{"attachment_id":80689000,"asset_id":71273197,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80689000/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="529893" href="https://independent.academia.edu/AdrianGambier">Adrian Gambier</a><script data-card-contents-for-user="529893" type="text/json">{"id":529893,"first_name":"Adrian","last_name":"Gambier","domain_name":"independent","page_name":"AdrianGambier","display_name":"Adrian Gambier","profile_url":"https://independent.academia.edu/AdrianGambier?f_ri=143163","photo":"https://0.academia-photos.com/529893/188247/19676806/s65_elrenegau.gambier.jpg"}</script></span></span></li><li class="js-paper-rank-work_71273197 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="71273197"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 71273197, container: ".js-paper-rank-work_71273197", }); });</script></li><li class="js-percentile-work_71273197 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget 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$(".js-view-count[data-work-id=71273197]").text(description); $(".js-view-count-work_71273197").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_71273197").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="71273197"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="15344" href="https://www.academia.edu/Documents/in/Robust_control">Robust control</a>, <script data-card-contents-for-ri="15344" type="text/json">{"id":15344,"name":"Robust control","url":"https://www.academia.edu/Documents/in/Robust_control?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a>, <script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="249165" href="https://www.academia.edu/Documents/in/Pid_Controller">Pid Controller</a>, <script data-card-contents-for-ri="249165" type="text/json">{"id":249165,"name":"Pid Controller","url":"https://www.academia.edu/Documents/in/Pid_Controller?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="276273" href="https://www.academia.edu/Documents/in/Reverse_osmosis">Reverse osmosis</a><script data-card-contents-for-ri="276273" type="text/json">{"id":276273,"name":"Reverse osmosis","url":"https://www.academia.edu/Documents/in/Reverse_osmosis?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=71273197]'), work: {"id":71273197,"title":"Control of a Reverse Osmosis plant by using a robust PID design based on multi-objective optimization","created_at":"2022-02-12T19:32:34.181-08:00","url":"https://www.academia.edu/71273197/Control_of_a_Reverse_Osmosis_plant_by_using_a_robust_PID_design_based_on_multi_objective_optimization?f_ri=143163","dom_id":"work_71273197","summary":null,"downloadable_attachments":[{"id":80689000,"asset_id":71273197,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":529893,"first_name":"Adrian","last_name":"Gambier","domain_name":"independent","page_name":"AdrianGambier","display_name":"Adrian Gambier","profile_url":"https://independent.academia.edu/AdrianGambier?f_ri=143163","photo":"https://0.academia-photos.com/529893/188247/19676806/s65_elrenegau.gambier.jpg"}],"research_interests":[{"id":15344,"name":"Robust control","url":"https://www.academia.edu/Documents/in/Robust_control?f_ri=143163","nofollow":false},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false},{"id":249165,"name":"Pid Controller","url":"https://www.academia.edu/Documents/in/Pid_Controller?f_ri=143163","nofollow":false},{"id":276273,"name":"Reverse osmosis","url":"https://www.academia.edu/Documents/in/Reverse_osmosis?f_ri=143163","nofollow":false},{"id":477865,"name":"Operant Conditioning","url":"https://www.academia.edu/Documents/in/Operant_Conditioning?f_ri=143163"},{"id":898062,"name":"Flow Rate","url":"https://www.academia.edu/Documents/in/Flow_Rate?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_68069817" data-work_id="68069817" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/68069817/Optimizing_Brain_Networks_Topologies_Using_Multi_objective_Evolutionary_Computation">Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_68069817" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for detecting and describing differences between brain networks and random and computationally optimized artificial networks. In this paper we use a multi-objective evolutionary optimization approach to generate optimized artificial</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/68069817" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6d1b235c4bbcf09d5efdc550ced1bf94" rel="nofollow" data-download="{"attachment_id":78679212,"asset_id":68069817,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78679212/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33605072" href="https://independent.academia.edu/ConchaBielza">Concha Bielza</a><script data-card-contents-for-user="33605072" type="text/json">{"id":33605072,"first_name":"Concha","last_name":"Bielza","domain_name":"independent","page_name":"ConchaBielza","display_name":"Concha Bielza","profile_url":"https://independent.academia.edu/ConchaBielza?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_68069817 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="68069817"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 68069817, container: ".js-paper-rank-work_68069817", }); 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u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="20099" href="https://www.academia.edu/Documents/in/Sensitivity_Analysis">Sensitivity Analysis</a>, <script data-card-contents-for-ri="20099" type="text/json">{"id":20099,"name":"Sensitivity Analysis","url":"https://www.academia.edu/Documents/in/Sensitivity_Analysis?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a>, <script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="151091" href="https://www.academia.edu/Documents/in/Nitrogen">Nitrogen</a>, <script 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fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1118777, container: ".js-paper-rank-work_1118777", }); });</script></li><li class="js-percentile-work_1118777 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1118777; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_1118777"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_1118777 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="1118777"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1118777; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1118777]").text(description); $(".js-view-count-work_1118777").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1118777").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1118777"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5748" href="https://www.academia.edu/Documents/in/Power_System">Power System</a>, <script data-card-contents-for-ri="5748" type="text/json">{"id":5748,"name":"Power System","url":"https://www.academia.edu/Documents/in/Power_System?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="22686" href="https://www.academia.edu/Documents/in/Distributed_System">Distributed System</a>, <script data-card-contents-for-ri="22686" type="text/json">{"id":22686,"name":"Distributed System","url":"https://www.academia.edu/Documents/in/Distributed_System?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="123410" href="https://www.academia.edu/Documents/in/Generation_Expansion_Planning">Generation Expansion Planning</a>, <script data-card-contents-for-ri="123410" type="text/json">{"id":123410,"name":"Generation Expansion Planning","url":"https://www.academia.edu/Documents/in/Generation_Expansion_Planning?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a><script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1118777]'), work: {"id":1118777,"title":"Optimal Distributed Generation placement in a restructured environment via a multi-objective optimization approach","created_at":"2011-11-27T03:50:04.301-08:00","url":"https://www.academia.edu/1118777/Optimal_Distributed_Generation_placement_in_a_restructured_environment_via_a_multi_objective_optimization_approach?f_ri=143163","dom_id":"work_1118777","summary":null,"downloadable_attachments":[{"id":12694077,"asset_id":1118777,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":986789,"first_name":"Payman","last_name":"Dehghanian","domain_name":"tamu","page_name":"PaymanDehghanian","display_name":"Payman Dehghanian","profile_url":"https://tamu.academia.edu/PaymanDehghanian?f_ri=143163","photo":"https://0.academia-photos.com/986789/1155930/1447623/s65_payman.dehghanian.jpg"}],"research_interests":[{"id":5748,"name":"Power System","url":"https://www.academia.edu/Documents/in/Power_System?f_ri=143163","nofollow":false},{"id":22686,"name":"Distributed System","url":"https://www.academia.edu/Documents/in/Distributed_System?f_ri=143163","nofollow":false},{"id":123410,"name":"Generation Expansion Planning","url":"https://www.academia.edu/Documents/in/Generation_Expansion_Planning?f_ri=143163","nofollow":false},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false},{"id":647249,"name":"Electric Power","url":"https://www.academia.edu/Documents/in/Electric_Power?f_ri=143163"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=143163"},{"id":860428,"name":"Distributed Generators","url":"https://www.academia.edu/Documents/in/Distributed_Generators?f_ri=143163"},{"id":886971,"name":"Electricity Generation","url":"https://www.academia.edu/Documents/in/Electricity_Generation?f_ri=143163"},{"id":1646121,"name":"Operation and Maintenance","url":"https://www.academia.edu/Documents/in/Operation_and_Maintenance?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_70317627 coauthored" data-work_id="70317627" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/70317627/Improved_normal_boundary_intersection_algorithm_A_method_for_energy_optimization_strategy_in_smart_buildings">Improved normal-boundary intersection algorithm: A method for energy optimization strategy in smart buildings</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">With the widespread use of distributed energy sources, the advantages of smart buildings over traditional buildings are becoming increasingly obvious. Subsequently, its energy optimal scheduling and multi-objective optimization have... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_70317627" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">With the widespread use of distributed energy sources, the advantages of smart buildings over traditional buildings are becoming increasingly obvious. Subsequently, its energy optimal scheduling and multi-objective optimization have become more and more complex and need to be solved urgently. This paper presents a novel method to optimize energy utilization in smart buildings. Firstly, multiple transfer-retention ratio (TRR) parameters are added to the evaluation of distributed renewable energy. Secondly, the normal-boundary intersection (NBI) algorithm is improved by the adaptive weight sum, the adjust uniform axes method, and Mahalanobis distance to form the improved normal-boundary intersection (INBI) algorithm. The multi-objective optimization problem in smart buildings is solved by the parameter TRR and INBI algorithm to improve the regulation efficiency. In response to the needs of decision-makers with evaluation indicators, the average deviation is reduced by 60% compared with the previous case. Numerical examples show that the proposed method is superior to the existing technologies in terms of three optimization objectives. The objectives include 8.2% reduction in equipment costs, 7.6% reduction in power supply costs, and 1.6% improvement in occupants' comfort.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/70317627" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="71593ef2263aed5237db48c305da18c3" rel="nofollow" data-download="{"attachment_id":80120466,"asset_id":70317627,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80120466/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="89972269" href="https://anl.academia.edu/YangLi">Yang Li</a><script data-card-contents-for-user="89972269" type="text/json">{"id":89972269,"first_name":"Yang","last_name":"Li","domain_name":"anl","page_name":"YangLi","display_name":"Yang Li","profile_url":"https://anl.academia.edu/YangLi?f_ri=143163","photo":"https://0.academia-photos.com/89972269/20398629/20070445/s65_yang.li.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-70317627">+1</span><div class="hidden js-additional-users-70317627"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MartinOnyekaOkoye">Martin Onyeka Okoye</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-70317627'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-70317627').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_70317627 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="70317627"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 70317627, container: ".js-paper-rank-work_70317627", }); });</script></li><li class="js-percentile-work_70317627 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 70317627; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_70317627"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_70317627 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="70317627"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 70317627; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=70317627]").text(description); $(".js-view-count-work_70317627").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_70317627").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="70317627"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2738" href="https://www.academia.edu/Documents/in/Renewable_Energy">Renewable Energy</a>, <script data-card-contents-for-ri="2738" type="text/json">{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3853" href="https://www.academia.edu/Documents/in/Optimization_Mathematics_">Optimization (Mathematics)</a>, <script data-card-contents-for-ri="3853" type="text/json">{"id":3853,"name":"Optimization (Mathematics)","url":"https://www.academia.edu/Documents/in/Optimization_Mathematics_?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9048" href="https://www.academia.edu/Documents/in/Scheduling">Scheduling</a>, <script data-card-contents-for-ri="9048" type="text/json">{"id":9048,"name":"Scheduling","url":"https://www.academia.edu/Documents/in/Scheduling?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a><script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=70317627]'), work: {"id":70317627,"title":"Improved normal-boundary intersection algorithm: A method for energy optimization strategy in smart buildings","created_at":"2022-02-03T04:49:46.436-08:00","url":"https://www.academia.edu/70317627/Improved_normal_boundary_intersection_algorithm_A_method_for_energy_optimization_strategy_in_smart_buildings?f_ri=143163","dom_id":"work_70317627","summary":"With the widespread use of distributed energy sources, the advantages of smart buildings over traditional buildings are becoming increasingly obvious. Subsequently, its energy optimal scheduling and multi-objective optimization have become more and more complex and need to be solved urgently. This paper presents a novel method to optimize energy utilization in smart buildings. Firstly, multiple transfer-retention ratio (TRR) parameters are added to the evaluation of distributed renewable energy. Secondly, the normal-boundary intersection (NBI) algorithm is improved by the adaptive weight sum, the adjust uniform axes method, and Mahalanobis distance to form the improved normal-boundary intersection (INBI) algorithm. The multi-objective optimization problem in smart buildings is solved by the parameter TRR and INBI algorithm to improve the regulation efficiency. In response to the needs of decision-makers with evaluation indicators, the average deviation is reduced by 60% compared with the previous case. Numerical examples show that the proposed method is superior to the existing technologies in terms of three optimization objectives. The objectives include 8.2% reduction in equipment costs, 7.6% reduction in power supply costs, and 1.6% improvement in occupants' comfort.","downloadable_attachments":[{"id":80120466,"asset_id":70317627,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":89972269,"first_name":"Yang","last_name":"Li","domain_name":"anl","page_name":"YangLi","display_name":"Yang Li","profile_url":"https://anl.academia.edu/YangLi?f_ri=143163","photo":"https://0.academia-photos.com/89972269/20398629/20070445/s65_yang.li.jpg"},{"id":214490430,"first_name":"Martin Onyeka","last_name":"Okoye","domain_name":"independent","page_name":"MartinOnyekaOkoye","display_name":"Martin Onyeka Okoye","profile_url":"https://independent.academia.edu/MartinOnyekaOkoye?f_ri=143163","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy?f_ri=143163","nofollow":false},{"id":3853,"name":"Optimization (Mathematics)","url":"https://www.academia.edu/Documents/in/Optimization_Mathematics_?f_ri=143163","nofollow":false},{"id":9048,"name":"Scheduling","url":"https://www.academia.edu/Documents/in/Scheduling?f_ri=143163","nofollow":false},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false},{"id":1089548,"name":"Normal Boundary Intersection","url":"https://www.academia.edu/Documents/in/Normal_Boundary_Intersection?f_ri=143163"},{"id":1147464,"name":"Smart Buildings","url":"https://www.academia.edu/Documents/in/Smart_Buildings?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11149149" data-work_id="11149149" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11149149/A_multi_objective_approach_for_the_prediction_of_loan_defaults">A multi-objective approach for the prediction of loan defaults</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines/models are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11149149" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines/models are incapable of handling the problems. This study applies the Fuzzy Simplex Generic Algorithm (a multi-objective optimization algorithm) in generating decision rules for predicting loan default in a typical credit institution. Empirical results show that the best indicators of default status are observed when repayment capacity and owners equity are low and the working capital is either low or high. Also, the two worst rule indicators are low repayment capacity, high owners’ equity and medium working capital or medium repayment capacity, low owners’ equity and high working capital.► We examine the difficulty of optimizing multiple goals in credit default forecast. ► This is problematic when competing decisions are being simultaneously optimized. ► Fuzzy Simplex Generic Algorithm is used to generate rules to predict loan defaults. ► The best indicator is low repayment capacity, owners’ equity, and working capital.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11149149" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b582c3d3f9e0da48598fa6c2343634ba" rel="nofollow" data-download="{"attachment_id":46858692,"asset_id":11149149,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46858692/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="26916147" href="https://independent.academia.edu/sanjoydas15">sanjoy das</a><script data-card-contents-for-user="26916147" type="text/json">{"id":26916147,"first_name":"sanjoy","last_name":"das","domain_name":"independent","page_name":"sanjoydas15","display_name":"sanjoy das","profile_url":"https://independent.academia.edu/sanjoydas15?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_11149149 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11149149"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11149149, container: ".js-paper-rank-work_11149149", }); });</script></li><li class="js-percentile-work_11149149 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 11149149; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_11149149"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_11149149 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="11149149"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11149149; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=11149149]").text(description); $(".js-view-count-work_11149149").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_11149149").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="11149149"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="7968" href="https://www.academia.edu/Documents/in/Prediction">Prediction</a>, <script data-card-contents-for-ri="7968" type="text/json">{"id":7968,"name":"Prediction","url":"https://www.academia.edu/Documents/in/Prediction?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="26066" href="https://www.academia.edu/Documents/in/Neural_Network">Neural Network</a>, <script data-card-contents-for-ri="26066" type="text/json">{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="27010" href="https://www.academia.edu/Documents/in/Fuzzy_Inference">Fuzzy Inference</a>, <script data-card-contents-for-ri="27010" type="text/json">{"id":27010,"name":"Fuzzy Inference","url":"https://www.academia.edu/Documents/in/Fuzzy_Inference?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="30329" href="https://www.academia.edu/Documents/in/Genetic_Algorithm">Genetic Algorithm</a><script data-card-contents-for-ri="30329" type="text/json">{"id":30329,"name":"Genetic Algorithm","url":"https://www.academia.edu/Documents/in/Genetic_Algorithm?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=11149149]'), work: {"id":11149149,"title":"A multi-objective approach for the prediction of loan defaults","created_at":"2015-02-27T11:13:13.548-08:00","url":"https://www.academia.edu/11149149/A_multi_objective_approach_for_the_prediction_of_loan_defaults?f_ri=143163","dom_id":"work_11149149","summary":"Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines/models are incapable of handling the problems. This study applies the Fuzzy Simplex Generic Algorithm (a multi-objective optimization algorithm) in generating decision rules for predicting loan default in a typical credit institution. Empirical results show that the best indicators of default status are observed when repayment capacity and owners equity are low and the working capital is either low or high. Also, the two worst rule indicators are low repayment capacity, high owners’ equity and medium working capital or medium repayment capacity, low owners’ equity and high working capital.► We examine the difficulty of optimizing multiple goals in credit default forecast. ► This is problematic when competing decisions are being simultaneously optimized. ► Fuzzy Simplex Generic Algorithm is used to generate rules to predict loan defaults. ► The best indicator is low repayment capacity, owners’ equity, and working capital.","downloadable_attachments":[{"id":46858692,"asset_id":11149149,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":26916147,"first_name":"sanjoy","last_name":"das","domain_name":"independent","page_name":"sanjoydas15","display_name":"sanjoy das","profile_url":"https://independent.academia.edu/sanjoydas15?f_ri=143163","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":7968,"name":"Prediction","url":"https://www.academia.edu/Documents/in/Prediction?f_ri=143163","nofollow":false},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network?f_ri=143163","nofollow":false},{"id":27010,"name":"Fuzzy Inference","url":"https://www.academia.edu/Documents/in/Fuzzy_Inference?f_ri=143163","nofollow":false},{"id":30329,"name":"Genetic Algorithm","url":"https://www.academia.edu/Documents/in/Genetic_Algorithm?f_ri=143163","nofollow":false},{"id":32433,"name":"Logistic Regression","url":"https://www.academia.edu/Documents/in/Logistic_Regression?f_ri=143163"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=143163"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163"},{"id":446579,"name":"Loan Default","url":"https://www.academia.edu/Documents/in/Loan_Default?f_ri=143163"},{"id":555139,"name":"Evolutionary optimization","url":"https://www.academia.edu/Documents/in/Evolutionary_optimization?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_62725662" data-work_id="62725662" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/62725662/Preference_Based_Multi_objective_Software_Modelling">Preference-Based Multi-objective Software Modelling</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we propose the use of preference-based evolutionary multi-objective optimization techniques (P-EMO) to address various software modelling challenges. P-EMO allows the incorporation of decision maker (i.e., designer)... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_62725662" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we propose the use of preference-based evolutionary multi-objective optimization techniques (P-EMO) to address various software modelling challenges. P-EMO allows the incorporation of decision maker (i.e., designer) preferences (e.g., quality, correctness, etc.) in multi-objective optimization techniques by restricting the Pareto front to a region of interest easing the decision making task. We discuss the different challenges and potential benefits of P-EMO in software modelling. We report experiments on the use of P-EMO on a well-known modeling problem where very promising results are obtained.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/62725662" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="95b007dc080babb9a4ded9fdc608fdae" rel="nofollow" data-download="{"attachment_id":75397175,"asset_id":62725662,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/75397175/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="41000372" href="https://rit.academia.edu/MohamedWiemMkaouer">Mohamed Wiem Mkaouer</a><script data-card-contents-for-user="41000372" type="text/json">{"id":41000372,"first_name":"Mohamed Wiem","last_name":"Mkaouer","domain_name":"rit","page_name":"MohamedWiemMkaouer","display_name":"Mohamed Wiem Mkaouer","profile_url":"https://rit.academia.edu/MohamedWiemMkaouer?f_ri=143163","photo":"https://0.academia-photos.com/41000372/23662621/22698059/s65_mohamed_wiem.mkaouer.jpg"}</script></span></span></li><li class="js-paper-rank-work_62725662 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="62725662"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 62725662, container: ".js-paper-rank-work_62725662", }); });</script></li><li class="js-percentile-work_62725662 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 62725662; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_62725662"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_62725662 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="62725662"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 62725662; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=62725662]").text(description); $(".js-view-count-work_62725662").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_62725662").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="62725662"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="449" href="https://www.academia.edu/Documents/in/Software_Engineering">Software Engineering</a>, <script data-card-contents-for-ri="449" type="text/json">{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2950" href="https://www.academia.edu/Documents/in/Computational_Modeling">Computational Modeling</a>, <script data-card-contents-for-ri="2950" type="text/json">{"id":2950,"name":"Computational Modeling","url":"https://www.academia.edu/Documents/in/Computational_Modeling?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="3523" href="https://www.academia.edu/Documents/in/Evolutionary_Computation">Evolutionary Computation</a>, <script data-card-contents-for-ri="3523" type="text/json">{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="13445" href="https://www.academia.edu/Documents/in/Multiobjective_Optimization">Multiobjective Optimization</a><script data-card-contents-for-ri="13445" type="text/json">{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=62725662]'), work: {"id":62725662,"title":"Preference-Based Multi-objective Software Modelling","created_at":"2021-11-29T15:47:24.731-08:00","url":"https://www.academia.edu/62725662/Preference_Based_Multi_objective_Software_Modelling?f_ri=143163","dom_id":"work_62725662","summary":"In this paper, we propose the use of preference-based evolutionary multi-objective optimization techniques (P-EMO) to address various software modelling challenges. P-EMO allows the incorporation of decision maker (i.e., designer) preferences (e.g., quality, correctness, etc.) in multi-objective optimization techniques by restricting the Pareto front to a region of interest easing the decision making task. We discuss the different challenges and potential benefits of P-EMO in software modelling. We report experiments on the use of P-EMO on a well-known modeling problem where very promising results are obtained.","downloadable_attachments":[{"id":75397175,"asset_id":62725662,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":41000372,"first_name":"Mohamed Wiem","last_name":"Mkaouer","domain_name":"rit","page_name":"MohamedWiemMkaouer","display_name":"Mohamed Wiem Mkaouer","profile_url":"https://rit.academia.edu/MohamedWiemMkaouer?f_ri=143163","photo":"https://0.academia-photos.com/41000372/23662621/22698059/s65_mohamed_wiem.mkaouer.jpg"}],"research_interests":[{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=143163","nofollow":false},{"id":2950,"name":"Computational Modeling","url":"https://www.academia.edu/Documents/in/Computational_Modeling?f_ri=143163","nofollow":false},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation?f_ri=143163","nofollow":false},{"id":13445,"name":"Multiobjective Optimization","url":"https://www.academia.edu/Documents/in/Multiobjective_Optimization?f_ri=143163","nofollow":false},{"id":39821,"name":"Search Based Software Engineering","url":"https://www.academia.edu/Documents/in/Search_Based_Software_Engineering?f_ri=143163"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=143163"},{"id":78326,"name":"Software modelling and simulation","url":"https://www.academia.edu/Documents/in/Software_modelling_and_simulation?f_ri=143163"},{"id":96446,"name":"Measurement","url":"https://www.academia.edu/Documents/in/Measurement?f_ri=143163"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163"},{"id":147488,"name":"Consumer Preferences","url":"https://www.academia.edu/Documents/in/Consumer_Preferences?f_ri=143163"},{"id":570997,"name":"Pareto front","url":"https://www.academia.edu/Documents/in/Pareto_front?f_ri=143163"},{"id":575013,"name":"NSGA II","url":"https://www.academia.edu/Documents/in/NSGA_II?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11692337" data-work_id="11692337" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11692337/Optimum_analysis_of_pavement_maintenance_using_multi_objective_genetic_algorithms">Optimum analysis of pavement maintenance using multi-objective genetic algorithms</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11692337" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5595f0a1dc840175a4bcdecb0c1a2cd6" rel="nofollow" data-download="{"attachment_id":46572385,"asset_id":11692337,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46572385/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="28629715" href="https://icmap.academia.edu/MohammadAmmar">Mohammad Ammar</a><script data-card-contents-for-user="28629715" type="text/json">{"id":28629715,"first_name":"Mohammad","last_name":"Ammar","domain_name":"icmap","page_name":"MohammadAmmar","display_name":"Mohammad Ammar","profile_url":"https://icmap.academia.edu/MohammadAmmar?f_ri=143163","photo":"https://0.academia-photos.com/28629715/8107937/9076306/s65_mohammad.ammar.jpg_oh_13a10dbdf020b181567b5f3a84a1bab0_oe_55b203ab___gda___1437776205_136415beb601c0a21932bf3fd71fe973"}</script></span></span></li><li class="js-paper-rank-work_11692337 InlineList-item 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Formulated &amp;quot; miMa &amp;quot; simulation model of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_63869419" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Paper presents an innovative method for finding the best setup of: race driver actions, vehicle motion trajectory, and vehicle chassis parameters for a closed-loop maneuver. Formulated &amp;quot; miMa &amp;quot; simulation model of driver-vehicle-road system with 26 generalized coordinates and more than 400 parameters is implemented for optimization with genetic algorithms. Numerical example considers Ford Focus ST170 (FWD) prepared for a track racing, which is negotiating a selected track part with RH corner. The combined optimization includes 28 decision variables. Two criteria, i.e. section time and exit velocity, are defined as optimization goals. Different strategies for improving performance of FWD car are found by using optimization.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/63869419" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31409332" href="https://pk.academia.edu/MichalManiowski">Michal Maniowski</a><script data-card-contents-for-user="31409332" type="text/json">{"id":31409332,"first_name":"Michal","last_name":"Maniowski","domain_name":"pk","page_name":"MichalManiowski","display_name":"Michal Maniowski","profile_url":"https://pk.academia.edu/MichalManiowski?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_63869419 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="63869419"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 63869419, container: ".js-paper-rank-work_63869419", }); 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Rangaiah","profile_url":"https://independent.academia.edu/GadeRangaiah?f_ri=143163","photo":"https://0.academia-photos.com/40861615/13586808/17888553/s65_g.p..rangaiah.jpg"}],"research_interests":[{"id":72,"name":"Chemical Engineering","url":"https://www.academia.edu/Documents/in/Chemical_Engineering?f_ri=143163","nofollow":false},{"id":14907,"name":"Industrial Biotechnology","url":"https://www.academia.edu/Documents/in/Industrial_Biotechnology?f_ri=143163","nofollow":false},{"id":46254,"name":"Optimization Problem","url":"https://www.academia.edu/Documents/in/Optimization_Problem?f_ri=143163","nofollow":false},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false},{"id":573653,"name":"Food Sciences","url":"https://www.academia.edu/Documents/in/Food_Sciences?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7484260" data-work_id="7484260" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7484260/Maintenance_optimization_models_and_criteria">Maintenance optimization models and criteria</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Due to widespread automation and the high capital tied up in production equipment, the importance of maintenance is ever increasing. This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_7484260" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Due to widespread automation and the high capital tied up in production equipment, the importance of maintenance is ever increasing. This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics have recognized this and many maintenance optimization models have been published over the years. Most of these models focus on one optimization criterion or objective, making multi-objective optimization models an underexplored area of maintenance optimization. Moreover, there is a big gap between academic models and application in practice. It is very difficult for industrial companies to adapt these models to their specific business context. This article reviews the literature on maintenance optimization models, with special focus on the optimization criteria and objectives used. To overcome flaws in present optimization models, a generic classification framework of maintenance optimization models is presented. All factors that have an influence on the optimization model will be made explicit and their links will be established. The framework is a starting point to develop business specific optimization models and enables decision making in e-maintenance. Moreover, it ensures a fit between the business model of a company and the maintenance optimization model right from the beginning. Future research will be on the development of a maintenance optimization model taking into account the most relevant optimization influence factors and criteria for a situation at hand.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7484260" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="73b01eca166033c8b2c02a4fcd8b0b8f" rel="nofollow" data-download="{"attachment_id":48454653,"asset_id":7484260,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/48454653/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="13351619" href="https://independent.academia.edu/Muchiri">Peter Muchiri</a><script data-card-contents-for-user="13351619" type="text/json">{"id":13351619,"first_name":"Peter","last_name":"Muchiri","domain_name":"independent","page_name":"Muchiri","display_name":"Peter Muchiri","profile_url":"https://independent.academia.edu/Muchiri?f_ri=143163","photo":"https://0.academia-photos.com/13351619/3742717/4384180/s65_peter.muchiri.jpg"}</script></span></span></li><li class="js-paper-rank-work_7484260 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="7484260"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 7484260, container: ".js-paper-rank-work_7484260", }); 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This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics have recognized this and many maintenance optimization models have been published over the years. Most of these models focus on one optimization criterion or objective, making multi-objective optimization models an underexplored area of maintenance optimization. Moreover, there is a big gap between academic models and application in practice. It is very difficult for industrial companies to adapt these models to their specific business context. This article reviews the literature on maintenance optimization models, with special focus on the optimization criteria and objectives used. To overcome flaws in present optimization models, a generic classification framework of maintenance optimization models is presented. All factors that have an influence on the optimization model will be made explicit and their links will be established. The framework is a starting point to develop business specific optimization models and enables decision making in e-maintenance. Moreover, it ensures a fit between the business model of a company and the maintenance optimization model right from the beginning. Future research will be on the development of a maintenance optimization model taking into account the most relevant optimization influence factors and criteria for a situation at hand.","downloadable_attachments":[{"id":48454653,"asset_id":7484260,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":13351619,"first_name":"Peter","last_name":"Muchiri","domain_name":"independent","page_name":"Muchiri","display_name":"Peter Muchiri","profile_url":"https://independent.academia.edu/Muchiri?f_ri=143163","photo":"https://0.academia-photos.com/13351619/3742717/4384180/s65_peter.muchiri.jpg"}],"research_interests":[{"id":1681,"name":"Decision Making","url":"https://www.academia.edu/Documents/in/Decision_Making?f_ri=143163","nofollow":false},{"id":128433,"name":"Business Model","url":"https://www.academia.edu/Documents/in/Business_Model?f_ri=143163","nofollow":false},{"id":143163,"name":"Multi objective 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u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="16478" href="https://www.academia.edu/Documents/in/Aircraft_Design">Aircraft Design</a>, <script data-card-contents-for-ri="16478" type="text/json">{"id":16478,"name":"Aircraft Design","url":"https://www.academia.edu/Documents/in/Aircraft_Design?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="35399" href="https://www.academia.edu/Documents/in/Control_system">Control system</a>, <script data-card-contents-for-ri="35399" type="text/json">{"id":35399,"name":"Control system","url":"https://www.academia.edu/Documents/in/Control_system?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a>, <script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="265625" href="https://www.academia.edu/Documents/in/Evolutionary_Algorithm">Evolutionary Algorithm</a><script data-card-contents-for-ri="265625" type="text/json">{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8433665]'), work: {"id":8433665,"title":"Wing and Airfoil Optimized Design of Transport 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itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/25022668/A_Hierarchical_Distributed_Control_for_Power_and_Performances_Optimization_of_Embedded_Systems">A Hierarchical Distributed Control for Power and Performances Optimization of Embedded Systems</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/25022668" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e0e732a0216bafe28e5374a8215094b6" rel="nofollow" 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window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_25022668"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_25022668 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="25022668"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25022668; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=25022668]").text(description); $(".js-view-count-work_25022668").attr('title', description).tooltip(); }); });</script></span><script>$(function() { 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Management","url":"https://www.academia.edu/Documents/in/Power_Management?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="73750" href="https://www.academia.edu/Documents/in/Distributed_Control">Distributed Control</a>, <script data-card-contents-for-ri="73750" type="text/json">{"id":73750,"name":"Distributed Control","url":"https://www.academia.edu/Documents/in/Distributed_Control?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a><script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=25022668]'), work: {"id":25022668,"title":"A Hierarchical Distributed Control for Power and Performances Optimization of Embedded Systems","created_at":"2016-05-04T06:32:56.055-07:00","url":"https://www.academia.edu/25022668/A_Hierarchical_Distributed_Control_for_Power_and_Performances_Optimization_of_Embedded_Systems?f_ri=143163","dom_id":"work_25022668","summary":null,"downloadable_attachments":[{"id":45346539,"asset_id":25022668,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32695942,"first_name":"David","last_name":"Siorpaes","domain_name":"st","page_name":"DSiorpaes","display_name":"David Siorpaes","profile_url":"https://st.academia.edu/DSiorpaes?f_ri=143163","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":11397,"name":"Energy Consumption","url":"https://www.academia.edu/Documents/in/Energy_Consumption?f_ri=143163","nofollow":false},{"id":11401,"name":"Power 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Optimization","url":"https://www.academia.edu/Documents/in/Performance_Optimization?f_ri=143163"},{"id":1764143,"name":"Device Driver","url":"https://www.academia.edu/Documents/in/Device_Driver?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_6722440" data-work_id="6722440" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/6722440/A_multi_objective_design_methodology_for_hybrid_renewable_energy_systems">A multi-objective design methodology for hybrid renewable energy systems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper describes a methodology to design a hybrid renewable energy system over a certain planning horizon. Traditionally a system plan was developed to achieve a minimum cost objective (MCO) while satisfying the energy demand,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_6722440" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper describes a methodology to design a hybrid renewable energy system over a certain planning horizon. Traditionally a system plan was developed to achieve a minimum cost objective (MCO) while satisfying the energy demand, reliability, stability and battery constraints. The minimum emissions objective (MEO) is now an important target to achieve subject to the above mentioned constraints. Each of the above problems may be solved using linear programming, but minimizing the two preceding objectives at the same time forms a multi-objective problem which is solved by the epsiv-constraint and the goal attainment methods. The epsiv-constraint method minimizes the total cost while the emissions are less than a certain value epsiv determined by the linear programming when minimizing emissions only or by the designer. The goal attainment method tries to balance all the objectives and make them as close as possible to the initial goals determined by MCO and MEO. A case study is presented to illustrate the applicability and the usefulness of the proposed method.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/6722440" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="11019787" href="https://aub-lb.academia.edu/SamiKaraki">Sami Karaki</a><script data-card-contents-for-user="11019787" type="text/json">{"id":11019787,"first_name":"Sami","last_name":"Karaki","domain_name":"aub-lb","page_name":"SamiKaraki","display_name":"Sami Karaki","profile_url":"https://aub-lb.academia.edu/SamiKaraki?f_ri=143163","photo":"https://0.academia-photos.com/11019787/164816922/154639992/s65_sami.karaki.jpeg"}</script></span></span></li><li class="js-paper-rank-work_6722440 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="6722440"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 6722440, container: ".js-paper-rank-work_6722440", }); 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$(".js-view-count[data-work-id=6722440]").text(description); $(".js-view-count-work_6722440").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_6722440").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="6722440"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2738" href="https://www.academia.edu/Documents/in/Renewable_Energy">Renewable Energy</a>, <script data-card-contents-for-ri="2738" type="text/json">{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4222" href="https://www.academia.edu/Documents/in/Hybrid_Systems">Hybrid Systems</a>, <script data-card-contents-for-ri="4222" type="text/json">{"id":4222,"name":"Hybrid Systems","url":"https://www.academia.edu/Documents/in/Hybrid_Systems?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5447" href="https://www.academia.edu/Documents/in/Linear_Programming">Linear Programming</a>, <script data-card-contents-for-ri="5447" type="text/json">{"id":5447,"name":"Linear Programming","url":"https://www.academia.edu/Documents/in/Linear_Programming?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="18186" href="https://www.academia.edu/Documents/in/Renewable_energy_resources">Renewable energy resources</a><script data-card-contents-for-ri="18186" type="text/json">{"id":18186,"name":"Renewable energy resources","url":"https://www.academia.edu/Documents/in/Renewable_energy_resources?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=6722440]'), work: {"id":6722440,"title":"A multi-objective design methodology for hybrid renewable energy systems","created_at":"2014-04-10T20:47:02.946-07:00","url":"https://www.academia.edu/6722440/A_multi_objective_design_methodology_for_hybrid_renewable_energy_systems?f_ri=143163","dom_id":"work_6722440","summary":"This paper describes a methodology to design a hybrid renewable energy system over a certain planning horizon. Traditionally a system plan was developed to achieve a minimum cost objective (MCO) while satisfying the energy demand, reliability, stability and battery constraints. The minimum emissions objective (MEO) is now an important target to achieve subject to the above mentioned constraints. Each of the above problems may be solved using linear programming, but minimizing the two preceding objectives at the same time forms a multi-objective problem which is solved by the epsiv-constraint and the goal attainment methods. The epsiv-constraint method minimizes the total cost while the emissions are less than a certain value epsiv determined by the linear programming when minimizing emissions only or by the designer. The goal attainment method tries to balance all the objectives and make them as close as possible to the initial goals determined by MCO and MEO. A case study is presented to illustrate the applicability and the usefulness of the proposed method.","downloadable_attachments":[],"ordered_authors":[{"id":11019787,"first_name":"Sami","last_name":"Karaki","domain_name":"aub-lb","page_name":"SamiKaraki","display_name":"Sami Karaki","profile_url":"https://aub-lb.academia.edu/SamiKaraki?f_ri=143163","photo":"https://0.academia-photos.com/11019787/164816922/154639992/s65_sami.karaki.jpeg"}],"research_interests":[{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy?f_ri=143163","nofollow":false},{"id":4222,"name":"Hybrid Systems","url":"https://www.academia.edu/Documents/in/Hybrid_Systems?f_ri=143163","nofollow":false},{"id":5447,"name":"Linear Programming","url":"https://www.academia.edu/Documents/in/Linear_Programming?f_ri=143163","nofollow":false},{"id":18186,"name":"Renewable energy resources","url":"https://www.academia.edu/Documents/in/Renewable_energy_resources?f_ri=143163","nofollow":false},{"id":25600,"name":"Stability","url":"https://www.academia.edu/Documents/in/Stability?f_ri=143163"},{"id":53223,"name":"renewable Energy sources","url":"https://www.academia.edu/Documents/in/renewable_Energy_sources?f_ri=143163"},{"id":76333,"name":"Energy demand","url":"https://www.academia.edu/Documents/in/Energy_demand?f_ri=143163"},{"id":96047,"name":"Case Study","url":"https://www.academia.edu/Documents/in/Case_Study?f_ri=143163"},{"id":122602,"name":"Renewable Energy System","url":"https://www.academia.edu/Documents/in/Renewable_Energy_System?f_ri=143163"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163"},{"id":159687,"name":"Design Methodology","url":"https://www.academia.edu/Documents/in/Design_Methodology?f_ri=143163"},{"id":679783,"name":"Boolean Satisfiability","url":"https://www.academia.edu/Documents/in/Boolean_Satisfiability?f_ri=143163"},{"id":838661,"name":"Photovoltaic systems","url":"https://www.academia.edu/Documents/in/Photovoltaic_systems?f_ri=143163"},{"id":1003508,"name":"Cost Function","url":"https://www.academia.edu/Documents/in/Cost_Function?f_ri=143163"},{"id":1451580,"name":"LINEAR PROGRAM","url":"https://www.academia.edu/Documents/in/LINEAR_PROGRAM?f_ri=143163"},{"id":2004933,"name":"Hybrid System","url":"https://www.academia.edu/Documents/in/Hybrid_System?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_69190361" data-work_id="69190361" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/69190361/Various_criteria_in_optimization_of_a_geothermal_air_conditioning_system_with_a_horizontal_ground_heat_exchanger">Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">... Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger Hoseyn Sayyaadi Ć,y and Emad Hadaddi Amlashi Faculty of Mechanical EngineeringEnergy Division, KN Toosi University of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_69190361" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">... Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger Hoseyn Sayyaadi Ć,y and Emad Hadaddi Amlashi Faculty of Mechanical EngineeringEnergy Division, KN Toosi University of Technology, Tehran, Iran SUMMARY ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/69190361" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="137397900" href="https://independent.academia.edu/EmadHadadiAmlashi">Emad Hadadi Amlashi</a><script data-card-contents-for-user="137397900" type="text/json">{"id":137397900,"first_name":"Emad","last_name":"Hadadi Amlashi","domain_name":"independent","page_name":"EmadHadadiAmlashi","display_name":"Emad Hadadi Amlashi","profile_url":"https://independent.academia.edu/EmadHadadiAmlashi?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_69190361 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="69190361"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 69190361, container: ".js-paper-rank-work_69190361", }); 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Various criteria in optimization of a geothermal air conditioning system with a horizontal ground heat exchanger Hoseyn Sayyaadi Ć,y and Emad Hadaddi Amlashi Faculty of Mechanical EngineeringEnergy Division, KN Toosi University of Technology, Tehran, Iran SUMMARY ...","downloadable_attachments":[],"ordered_authors":[{"id":137397900,"first_name":"Emad","last_name":"Hadadi Amlashi","domain_name":"independent","page_name":"EmadHadadiAmlashi","display_name":"Emad Hadadi Amlashi","profile_url":"https://independent.academia.edu/EmadHadadiAmlashi?f_ri=143163","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=143163","nofollow":false},{"id":72,"name":"Chemical Engineering","url":"https://www.academia.edu/Documents/in/Chemical_Engineering?f_ri=143163","nofollow":false},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=143163","nofollow":false},{"id":522,"name":"Thermodynamics","url":"https://www.academia.edu/Documents/in/Thermodynamics?f_ri=143163","nofollow":false},{"id":5412,"name":"Energy","url":"https://www.academia.edu/Documents/in/Energy?f_ri=143163"},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=143163"},{"id":19517,"name":"Heat Exchanger","url":"https://www.academia.edu/Documents/in/Heat_Exchanger?f_ri=143163"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm?f_ri=143163"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=143163"},{"id":56060,"name":"Thermoeconomics","url":"https://www.academia.edu/Documents/in/Thermoeconomics?f_ri=143163"},{"id":103356,"name":"Exergy","url":"https://www.academia.edu/Documents/in/Exergy?f_ri=143163"},{"id":116306,"name":"Air Conditioning","url":"https://www.academia.edu/Documents/in/Air_Conditioning?f_ri=143163"},{"id":134767,"name":"Exergy Analysis","url":"https://www.academia.edu/Documents/in/Exergy_Analysis?f_ri=143163"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm?f_ri=143163"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_68441075" data-work_id="68441075" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/68441075/Robust_Design_and_Parametric_Performance_Study_of_an_Automotive_Fan_Blade_by_Coupling_Multi_Objective_Genetic_Optimization_and_Flow_Parameterization">Robust Design and Parametric Performance Study of an Automotive Fan Blade by Coupling Multi-Objective Genetic Optimization and Flow Parameterization</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Optimal design techniques are not routinely used in the industry when dealing with complex physical phenomena, due to high computing costs. The parameterization method described in this paper is based on the differentiation and high-order... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_68441075" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Optimal design techniques are not routinely used in the industry when dealing with complex physical phenomena, due to high computing costs. The parameterization method described in this paper is based on the differentiation and high-order Taylor-series expansion of the discretized Reynolds-Averaged Navier-Stokes equations. A flow database containing the derivatives of the physical variables with respect to the design variables is produced by the Turb’Opty c © parameterization tool and thoroughly explored by a multi-objective Genetic Algorithm coupled to the extrapolation tool Turb’Post c . The optimization case of an automotive engine cooling fan blade is fully described. Five geometric parameters have been chosen to characterize the fan blade. Three objective functions have been taken into account: the minimization of the loss coefficient, the maximization of the static pressure rise and the minimization of the torque. Two geometric constraints have been imposed to the extrapolated...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/68441075" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="85b273e40a03e15ecc37c88ea1d94ab5" rel="nofollow" data-download="{"attachment_id":78914400,"asset_id":68441075,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78914400/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="179427" href="https://usherbrooke.academia.edu/StephaneMoreau">Stephane Moreau</a><script data-card-contents-for-user="179427" type="text/json">{"id":179427,"first_name":"Stephane","last_name":"Moreau","domain_name":"usherbrooke","page_name":"StephaneMoreau","display_name":"Stephane Moreau","profile_url":"https://usherbrooke.academia.edu/StephaneMoreau?f_ri=143163","photo":"https://0.academia-photos.com/179427/3767666/4410929/s65_stephane.moreau.jpg"}</script></span></span></li><li class="js-paper-rank-work_68441075 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="68441075"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 68441075, container: ".js-paper-rank-work_68441075", }); 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$(".js-view-count[data-work-id=68441075]").text(description); $(".js-view-count-work_68441075").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_68441075").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="68441075"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">13</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="156" href="https://www.academia.edu/Documents/in/Genetics">Genetics</a>, <script data-card-contents-for-ri="156" type="text/json">{"id":156,"name":"Genetics","url":"https://www.academia.edu/Documents/in/Genetics?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2298" href="https://www.academia.edu/Documents/in/Computational_Fluid_Dynamics">Computational Fluid Dynamics</a>, <script data-card-contents-for-ri="2298" type="text/json">{"id":2298,"name":"Computational Fluid Dynamics","url":"https://www.academia.edu/Documents/in/Computational_Fluid_Dynamics?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="30329" href="https://www.academia.edu/Documents/in/Genetic_Algorithm">Genetic Algorithm</a>, <script data-card-contents-for-ri="30329" type="text/json">{"id":30329,"name":"Genetic Algorithm","url":"https://www.academia.edu/Documents/in/Genetic_Algorithm?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="48652" href="https://www.academia.edu/Documents/in/Shape_Optimization">Shape Optimization</a><script data-card-contents-for-ri="48652" type="text/json">{"id":48652,"name":"Shape Optimization","url":"https://www.academia.edu/Documents/in/Shape_Optimization?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=68441075]'), work: {"id":68441075,"title":"Robust Design and Parametric Performance Study of an Automotive Fan Blade by Coupling Multi-Objective Genetic Optimization and Flow Parameterization","created_at":"2022-01-16T13:48:20.674-08:00","url":"https://www.academia.edu/68441075/Robust_Design_and_Parametric_Performance_Study_of_an_Automotive_Fan_Blade_by_Coupling_Multi_Objective_Genetic_Optimization_and_Flow_Parameterization?f_ri=143163","dom_id":"work_68441075","summary":"Optimal design techniques are not routinely used in the industry when dealing with complex physical phenomena, due to high computing costs. The parameterization method described in this paper is based on the differentiation and high-order Taylor-series expansion of the discretized Reynolds-Averaged Navier-Stokes equations. A flow database containing the derivatives of the physical variables with respect to the design variables is produced by the Turb’Opty c © parameterization tool and thoroughly explored by a multi-objective Genetic Algorithm coupled to the extrapolation tool Turb’Post c . The optimization case of an automotive engine cooling fan blade is fully described. Five geometric parameters have been chosen to characterize the fan blade. Three objective functions have been taken into account: the minimization of the loss coefficient, the maximization of the static pressure rise and the minimization of the torque. Two geometric constraints have been imposed to the extrapolated...","downloadable_attachments":[{"id":78914400,"asset_id":68441075,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":179427,"first_name":"Stephane","last_name":"Moreau","domain_name":"usherbrooke","page_name":"StephaneMoreau","display_name":"Stephane Moreau","profile_url":"https://usherbrooke.academia.edu/StephaneMoreau?f_ri=143163","photo":"https://0.academia-photos.com/179427/3767666/4410929/s65_stephane.moreau.jpg"}],"research_interests":[{"id":156,"name":"Genetics","url":"https://www.academia.edu/Documents/in/Genetics?f_ri=143163","nofollow":false},{"id":2298,"name":"Computational Fluid Dynamics","url":"https://www.academia.edu/Documents/in/Computational_Fluid_Dynamics?f_ri=143163","nofollow":false},{"id":30329,"name":"Genetic Algorithm","url":"https://www.academia.edu/Documents/in/Genetic_Algorithm?f_ri=143163","nofollow":false},{"id":48652,"name":"Shape Optimization","url":"https://www.academia.edu/Documents/in/Shape_Optimization?f_ri=143163","nofollow":false},{"id":91603,"name":"Computation Fluid Dynamics","url":"https://www.academia.edu/Documents/in/Computation_Fluid_Dynamics?f_ri=143163"},{"id":118070,"name":"Turbomachinery","url":"https://www.academia.edu/Documents/in/Turbomachinery?f_ri=143163"},{"id":129770,"name":"Key words","url":"https://www.academia.edu/Documents/in/Key_words?f_ri=143163"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163"},{"id":214482,"name":"Robust Design","url":"https://www.academia.edu/Documents/in/Robust_Design?f_ri=143163"},{"id":789521,"name":"Optimal Design","url":"https://www.academia.edu/Documents/in/Optimal_Design?f_ri=143163"},{"id":891680,"name":"Fan Blade Design","url":"https://www.academia.edu/Documents/in/Fan_Blade_Design?f_ri=143163"},{"id":1450944,"name":"Multi objective genetic algorithm optimisation","url":"https://www.academia.edu/Documents/in/Multi_objective_genetic_algorithm_optimisation?f_ri=143163"},{"id":2421822,"name":"Objective function","url":"https://www.academia.edu/Documents/in/Objective_function?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4563826" data-work_id="4563826" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4563826/A_multi_objective_design_methodology_for_hybrid_renewable_energy_systems">A multi-objective design methodology for hybrid renewable energy systems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper describes a methodology to design a hybrid renewable energy system over a certain planning horizon. Traditionally a system plan was developed to achieve a minimum cost objective (MCO) while satisfying the energy demand,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4563826" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper describes a methodology to design a hybrid renewable energy system over a certain planning horizon. Traditionally a system plan was developed to achieve a minimum cost objective (MCO) while satisfying the energy demand, reliability, stability and battery constraints. The minimum emissions objective (MEO) is now an important target to achieve subject to the above mentioned constraints. Each of the above problems may be solved using linear programming, but minimizing the two preceding objectives at the same time forms a multi-objective problem which is solved by the epsiv-constraint and the goal attainment methods. The epsiv-constraint method minimizes the total cost while the emissions are less than a certain value epsiv determined by the linear programming when minimizing emissions only or by the designer. The goal attainment method tries to balance all the objectives and make them as close as possible to the initial goals determined by MCO and MEO. A case study is presented to illustrate the applicability and the usefulness of the proposed method.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4563826" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1b7c9cdeccd2b5c5ea462b17362c145f" rel="nofollow" data-download="{"attachment_id":31939802,"asset_id":4563826,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/31939802/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5710278" href="https://independent.academia.edu/ahmadrifai10">ahmad rifai</a><script data-card-contents-for-user="5710278" type="text/json">{"id":5710278,"first_name":"ahmad","last_name":"rifai","domain_name":"independent","page_name":"ahmadrifai10","display_name":"ahmad rifai","profile_url":"https://independent.academia.edu/ahmadrifai10?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_4563826 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4563826"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4563826, container: ".js-paper-rank-work_4563826", }); 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$(".js-view-count[data-work-id=4563826]").text(description); $(".js-view-count-work_4563826").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_4563826").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="4563826"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2738" href="https://www.academia.edu/Documents/in/Renewable_Energy">Renewable Energy</a>, <script data-card-contents-for-ri="2738" type="text/json">{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4222" href="https://www.academia.edu/Documents/in/Hybrid_Systems">Hybrid Systems</a>, <script data-card-contents-for-ri="4222" type="text/json">{"id":4222,"name":"Hybrid Systems","url":"https://www.academia.edu/Documents/in/Hybrid_Systems?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5447" href="https://www.academia.edu/Documents/in/Linear_Programming">Linear Programming</a>, <script data-card-contents-for-ri="5447" type="text/json">{"id":5447,"name":"Linear Programming","url":"https://www.academia.edu/Documents/in/Linear_Programming?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="18186" href="https://www.academia.edu/Documents/in/Renewable_energy_resources">Renewable energy resources</a><script data-card-contents-for-ri="18186" type="text/json">{"id":18186,"name":"Renewable energy resources","url":"https://www.academia.edu/Documents/in/Renewable_energy_resources?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=4563826]'), work: {"id":4563826,"title":"A multi-objective design methodology for hybrid renewable energy systems","created_at":"2013-09-23T10:38:30.133-07:00","url":"https://www.academia.edu/4563826/A_multi_objective_design_methodology_for_hybrid_renewable_energy_systems?f_ri=143163","dom_id":"work_4563826","summary":"This paper describes a methodology to design a hybrid renewable energy system over a certain planning horizon. Traditionally a system plan was developed to achieve a minimum cost objective (MCO) while satisfying the energy demand, reliability, stability and battery constraints. The minimum emissions objective (MEO) is now an important target to achieve subject to the above mentioned constraints. Each of the above problems may be solved using linear programming, but minimizing the two preceding objectives at the same time forms a multi-objective problem which is solved by the epsiv-constraint and the goal attainment methods. The epsiv-constraint method minimizes the total cost while the emissions are less than a certain value epsiv determined by the linear programming when minimizing emissions only or by the designer. The goal attainment method tries to balance all the objectives and make them as close as possible to the initial goals determined by MCO and MEO. A case study is presented to illustrate the applicability and the usefulness of the proposed method.","downloadable_attachments":[{"id":31939802,"asset_id":4563826,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5710278,"first_name":"ahmad","last_name":"rifai","domain_name":"independent","page_name":"ahmadrifai10","display_name":"ahmad rifai","profile_url":"https://independent.academia.edu/ahmadrifai10?f_ri=143163","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2738,"name":"Renewable Energy","url":"https://www.academia.edu/Documents/in/Renewable_Energy?f_ri=143163","nofollow":false},{"id":4222,"name":"Hybrid Systems","url":"https://www.academia.edu/Documents/in/Hybrid_Systems?f_ri=143163","nofollow":false},{"id":5447,"name":"Linear Programming","url":"https://www.academia.edu/Documents/in/Linear_Programming?f_ri=143163","nofollow":false},{"id":18186,"name":"Renewable energy resources","url":"https://www.academia.edu/Documents/in/Renewable_energy_resources?f_ri=143163","nofollow":false},{"id":25600,"name":"Stability","url":"https://www.academia.edu/Documents/in/Stability?f_ri=143163"},{"id":53223,"name":"renewable Energy sources","url":"https://www.academia.edu/Documents/in/renewable_Energy_sources?f_ri=143163"},{"id":76333,"name":"Energy demand","url":"https://www.academia.edu/Documents/in/Energy_demand?f_ri=143163"},{"id":96047,"name":"Case Study","url":"https://www.academia.edu/Documents/in/Case_Study?f_ri=143163"},{"id":122602,"name":"Renewable Energy System","url":"https://www.academia.edu/Documents/in/Renewable_Energy_System?f_ri=143163"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163"},{"id":159687,"name":"Design Methodology","url":"https://www.academia.edu/Documents/in/Design_Methodology?f_ri=143163"},{"id":679783,"name":"Boolean Satisfiability","url":"https://www.academia.edu/Documents/in/Boolean_Satisfiability?f_ri=143163"},{"id":838661,"name":"Photovoltaic systems","url":"https://www.academia.edu/Documents/in/Photovoltaic_systems?f_ri=143163"},{"id":1003508,"name":"Cost Function","url":"https://www.academia.edu/Documents/in/Cost_Function?f_ri=143163"},{"id":1451580,"name":"LINEAR PROGRAM","url":"https://www.academia.edu/Documents/in/LINEAR_PROGRAM?f_ri=143163"},{"id":2004933,"name":"Hybrid System","url":"https://www.academia.edu/Documents/in/Hybrid_System?f_ri=143163"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11666060" data-work_id="11666060" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11666060/A_Review_and_Evaluation_of_Multiobjective_Algorithms_for_the_Flowshop_Scheduling_Problem">A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11666060" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5902482d4a9955afd72a6f20a32b5303" rel="nofollow" data-download="{"attachment_id":46583158,"asset_id":11666060,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46583158/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="28531327" href="https://polimi.academia.edu/MicheleCiavotta">Michele Ciavotta</a><script data-card-contents-for-user="28531327" type="text/json">{"id":28531327,"first_name":"Michele","last_name":"Ciavotta","domain_name":"polimi","page_name":"MicheleCiavotta","display_name":"Michele Ciavotta","profile_url":"https://polimi.academia.edu/MicheleCiavotta?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_11666060 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11666060"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11666060, container: ".js-paper-rank-work_11666060", }); });</script></li><li class="js-percentile-work_11666060 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 11666060; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_11666060"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_11666060 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="11666060"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11666060; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=11666060]").text(description); $(".js-view-count-work_11666060").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_11666060").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="11666060"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="80414" href="https://www.academia.edu/Documents/in/Mathematical_Sciences">Mathematical Sciences</a>, <script data-card-contents-for-ri="80414" type="text/json">{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="112574" href="https://www.academia.edu/Documents/in/Goal_programming">Goal programming</a>, <script data-card-contents-for-ri="112574" type="text/json">{"id":112574,"name":"Goal programming","url":"https://www.academia.edu/Documents/in/Goal_programming?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="143163" href="https://www.academia.edu/Documents/in/Multi_objective_optimization">Multi objective optimization</a>, <script data-card-contents-for-ri="143163" type="text/json">{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization?f_ri=143163","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="289017" href="https://www.academia.edu/Documents/in/Comparative_method">Comparative method</a><script data-card-contents-for-ri="289017" type="text/json">{"id":289017,"name":"Comparative method","url":"https://www.academia.edu/Documents/in/Comparative_method?f_ri=143163","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=11666060]'), work: {"id":11666060,"title":"A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling 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href="https://www.academia.edu/15632579/Wing_and_Airfoil_Optimized_Design_of_Transport_Aircraft">Wing and Airfoil Optimized Design of Transport Aircraft</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">An efficient methodology for multi-disciplinary design and optimization of transport was elaborated and developed. The methodology was implemented in a commercial known optimization framework. Semi-empirical methods were employed for wing... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15632579" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">An efficient methodology for multi-disciplinary design and optimization of transport was elaborated and developed. The methodology was implemented in a commercial known optimization framework. Semi-empirical methods were employed for wing weight estimation; a multi-block full-potential code was used for drag calculation; Vortex Lattice method was implemented for spanwise lift distribution in order to compute de aircraft maximum-lift coefficient via critical</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15632579" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1ffc374c25b8b0c03404a6fe93975658" rel="nofollow" data-download="{"attachment_id":38747847,"asset_id":15632579,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/38747847/download_file?st=MTczMjcyNjUyOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34810116" href="https://ita-br.academia.edu/PPaglione">Pedro Paglione</a><script data-card-contents-for-user="34810116" type="text/json">{"id":34810116,"first_name":"Pedro","last_name":"Paglione","domain_name":"ita-br","page_name":"PPaglione","display_name":"Pedro Paglione","profile_url":"https://ita-br.academia.edu/PPaglione?f_ri=143163","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-15632579">+1</span><div class="hidden js-additional-users-15632579"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/BentoMattos">Bento Mattos</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-15632579'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-15632579').html(); 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