An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants

<!DOCTYPE html>    <html>  <head> <meta charset="utf-8" /> <title>An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants</title>  <link rel="shortcut icon" type="image/ico" href="./data/ijsom/coversheet/favicon.ico" />  <meta name="viewport" content="width=device-width, maximum-scale=1, initial-scale=1, user-scalable=0" />   <meta name="keywords" content="Particle swarm optimization,Taxonomy,PSO variants,Expert system,Knowledge base" /> <meta name="description" content="Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration." /> <meta name="title" content="An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants" /> <meta name="googlebot" content="NOODP" /> <meta name="citation_title" content="An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants" /> <meta name="citation_author" content="Masehian, Ellips" /> <meta name="citation_author_institution" content="Tarbiat Modares University, Teahran, Iran" /> <meta name="citation_author" content="Eghbal Akhlaghi, Vahid" /> <meta name="citation_author_institution" content="Middle East Technical University, Ankara, Turkey" /> <meta name="citation_author" content="Akbaripour, Hossein" /> <meta name="citation_author_institution" content="Tarbiat Modares University, Teahran, Iran" /> <meta name="citation_author" content="Sedighizadeh, Davoud" /> <meta name="citation_author_institution" content="Islamic Azad University, Saveh branch, saveh, Iran" /> <meta name="citation_abstract" content="Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration." /> <meta name="citation_id" content="2351" /> <meta name="citation_publication_date" content="2015/05/01" /> <meta name="citation_date" content="2015-05-01" /> <meta name="citation_journal_title" content="International Journal of Supply and Operations Management" /> <meta name="citation_issn" content="23831359" /> <meta name="citation_volume" content="2" /> <meta name="citation_issue" content="1" /> <meta name="citation_firstpage" content="569" /> <meta name="citation_lastpage" content="594" /> <meta name="citation_publisher" content="Kharazmi University" /> <meta name="citation_doi" content="10.22034/2015.1.03" /> <meta name="DC.Identifier" content="10.22034/2015.1.03" /> <meta name="citation_abstract_html_url" content="http://www.ijsom.com/article_2351.html" /> <meta name="citation_pdf_url" content="http://www.ijsom.com/article_2351_1936390fb2dce7705ee4fab3e3240681.pdf" /> <meta name="DC.Title" content="An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants" /> <meta name="DC.Source" content="International Journal of Supply and Operations Management" /> <meta name="DC.Date" content="01/05/2015" /> <meta name="DC.Date.issued" content="2015-05-01" /> <meta name="DC.Format" content="application/pdf" /> <meta name="DC.Contributor" content="Masehian, Ellips" /> <meta name="DC.Contributor" content="Eghbal Akhlaghi, Vahid" /> <meta name="DC.Contributor" content="Akbaripour, Hossein" /> <meta name="DC.Contributor" content="Sedighizadeh, Davoud" /> <meta name="og:title" content="An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants" /> <meta name="og:description" content="Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. 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margin-bottom-6 ltr"> <li class="padding-3"> <a href="./?_action=article&au=109387&_au=Ellips++Masehian">Ellips Masehian</a> <sup><a href="mailto:masehian@modares.ac.ir" data-toggle="tooltip" data-placement="bottom" title="Email to Corresponding Author"><i class="fa fa-envelope-o" ></i></a></sup> <sup><a href="#aff1" >1</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=109781&_au=Vahid++Eghbal+Akhlaghi">Vahid Eghbal Akhlaghi</a> <sup><a href="#aff2" >2</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=109780&_au=Hossein++Akbaripour">Hossein Akbaripour</a> <sup><a href="#aff1" >1</a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=112067&_au=Davoud++Sedighizadeh">Davoud Sedighizadeh</a> <sup><a href="#aff3" >3</a></sup> </li> </ul> <p class="margin-bottom-3 ltr" id="aff1"> <sup>1</sup> Tarbiat Modares University, Teahran, Iran </p> <p class="margin-bottom-3 ltr" id="aff2"> <sup>2</sup> Middle East Technical University, Ankara, Turkey </p> <p class="margin-bottom-3 ltr" id="aff3"> <sup>3</sup> Islamic Azad University, Saveh branch, saveh, Iran </p> <div class="margin-bottom-3 ltr" id="ar_doi" title="DOI"><i class="ai ai-doi size-25 text-orange"></i> <span dir="ltr"><a href="https://dx.doi.org/10.22034/2015.1.03">10.22034/2015.1.03</a></span></div> <p style="margin:12px -2px 0 -2px"><strong>Abstract</strong></p> <div class="padding_abstract justify ltr">Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration.</div> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Keywords</strong></p> <ul class="block list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=104003&_kw=Particle+swarm+optimization" >Particle swarm optimization</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=6766&_kw=Taxonomy" >Taxonomy</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=104004&_kw=PSO+variants" >PSO variants</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=104005&_kw=Expert+system" >Expert system</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=104006&_kw=Knowledge+base" >Knowledge base</a> </li> </ul> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Main Subjects</strong></p> <ul class="block list-inline list-inline-seprator margin-bottom-6"> <li class="padding-3"> <a href="./?_action=article&sb=159&_sb=artificial+intelligence+%26amp%3B+expert+system" >artificial intelligence & expert system</a> </li> </ul> </div> <hr> <div class="page_break"></div> <div class="panel"> <div class="panel-heading card-header"> <h4 class="panel-title "> <a data-toggle="collapse" data-parent="#accordions" href="#collapsesRef"><i class="fa fa-plus"></i> References</a> </h4> </div> <div id="collapsesRef" class="panel-collapse collapse"> <div class="panel-body justify"> <div class="padding-3 margin-top-3 ltr justify">Aguirre A. 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<ul class="list-group list-group-bordered list-group-noicon" style="display:block !important;max-height:9999px"> <li class="list-group-item"><a class="tag_a">Article View: <i>5,240</i></a></li> <li class="list-group-item"><a class="tag_a">PDF Download: <i>2,760</i></a></li> </ul> </div> </div> </div> </div>  </div> </section> <div id="cite-apa" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">APA</h4> </div>  <div class="modal-body"> <p>Masehian, E., Eghbal Akhlaghi, V., Akbaripour, H., & Sedighizadeh, D. (2015). An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants. <em>International Journal of Supply and Operations Management</em>, 2(1), 569-594. doi: 10.22034/2015.1.03</p> </div> </div> </div> </div> <div id="cite-mla" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">MLA</h4> </div>  <div class="modal-body"> <p>Ellips Masehian; Vahid Eghbal Akhlaghi; Hossein Akbaripour; Davoud Sedighizadeh. "An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants". <em>International Journal of Supply and Operations Management</em>, 2, 1, 2015, 569-594. doi: 10.22034/2015.1.03</p> </div> </div> </div> </div> <div id="cite-harvard" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">HARVARD</h4> </div>  <div class="modal-body"> <p>Masehian, E., Eghbal Akhlaghi, V., Akbaripour, H., Sedighizadeh, D. (2015). 'An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants', <em>International Journal of Supply and Operations Management</em>, 2(1), pp. 569-594. doi: 10.22034/2015.1.03</p> </div> </div> </div> </div> <div id="cite-vancouver" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">VANCOUVER</h4> </div>  <div class="modal-body"> <p>Masehian, E., Eghbal Akhlaghi, V., Akbaripour, H., Sedighizadeh, D. An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants. <em>International Journal of Supply and Operations Management</em>, 2015; 2(1): 569-594. doi: 10.22034/2015.1.03</p> </div> </div> </div> </div> </div> </div>   <section class="alternate padding-xxs"> </section>   <div class="container"> <footer id="footer"> <div class="scrollup" id="scroll" href="#"><span></span></div> <div class="row"> <div class="col-md-2">  <h4 class="">Explore Journal</h4> <ul class="footer-links list-unstyled"> <li id="fli_home"><a href="./">Home</a></li> <li id="fli_about"><a href="./journal/about">About Journal</a></li> <li id="fli_Edb"><a href="./journal/editorial.board">Editorial Board</a></li> <li id="fli_submit"><a href="./author">Submit Manuscript</a></li> <li id="fli_contactus"><a href="./journal/contact.us">Contact Us</a></li> <li id="fli_glossary"><a href="./journal/glossary">Glossary</a></li> <li id="fli_order_hrdj"><a href="./journal/subscription.form">Hard Copy Subscription</a></li> <li id="fli_sitemap"><a href="./sitemap.xml?usr">Sitemap</a></li> </ul>  </div> <div class="col-md-3">  <h4 class="">Latest News</h4> <ul class="footer-posts list-unstyled"> <li> <a href="./news?newsCode=173">SD of ISC: Sustainable Development of Intelligent Supply Chains based on Trends and Future Directions: Application of Novel Solution Techniques</a> <small class="ltr">2023-03-05</small> </li> </ul>  </div> <div class="col-md-3">  <div><p><a title="Linkedin" href="http://www.linkedin.com/company/ijsom?trk=eml-cp_mktg-btn-welcome-20120607%2F"><img src="images/linkedin.jpg" alt="linkedin" /></a></p></div>  </div> <div class="col-md-4">  <h4 class="">Newsletter Subscription</h4> <p>Subscribe to the journal newsletter and receive the latest news and updates</p> <form class="validate" action="" method="post" data-success="Subscription saved successfully." data-toastr-position="bottom-right"> <input type="hidden" name="_token" value="fbc7e23c12cac43d7395d903a03ced191d6b39ee4f32e7d2"/> <div class="input-group"> <span class="input-group-addon"><i class="fa fa-envelope"></i></span> <input type="email" id="email" name="email" required="required" class="form-control required sbs_email" placeholder="Enter your Email" oninvalid="this.setCustomValidity('Enter a valid email address.')" oninput="this.setCustomValidity('')"> <span class="input-group-btn"> <button class="btn btn-primary mybtn" type="submit">Subscribe</button> </span> </div> </form>   <div class="margin-top-20"> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Facebook"> <i class="fa fa-facebook-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Twitter"> <i class="fa fa-twitter-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Linkedin"> <i class="fa fa-linkedin-square" aria-hidden="true"></i> </a> <a class="noborder" href="./ju.rss" class="social-icon social-icon-border social-rss pull-left block" data-toggle="tooltip" data-placement="top" title="Rss"><i class="fa fa-rss-square" aria-hidden="true"></i></a> </div> </div> </div> <div class="copyright" style="position: relative"> <ul class="nomargin list-inline mobile-block"> <li>© Journal Management System. <span id='sp_crt'>Powered by <a target='_blank' href='https://www.sinaweb.net/'>Sinaweb</a></span></li> </ul> </div> </footer> </div>  </div>   <a href="#" id="toTop_old"></a>  <div id="preloader"> <div class="inner"> <span class="loader"></span> </div> </div>  <script type="text/javascript">var plugin_path = './themes/base/front/assets/plugins/';</script> <script type="text/javascript" src="./themes/base/front/assets/js/scripts.js?v=0.02"></script>   <script type="text/javascript"> $('ul.nav li.dropdown').hover(function() { if (window.matchMedia('(max-width: 767px)').matches) return; 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An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants