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Conference Paper: Parameter sensitivity analysis of social spider algorithm

TitleParameter sensitivity analysis of social spider algorithm
Authors
KeywordsSocial spider algorithm
Global optimization
Parameter sensitivity analysis
Evolutionary computation
Meta-heuristic
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
Citation
The 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Sendai, Japan, 25-28 May 2015. In Conference Proceedings, 2015, p. 3200-3205 How to Cite?
AbstractSocial Spider Algorithm (SSA) is a recently proposed general-purpose real-parameter metaheuristic designed to solve global numerical optimization problems. This work systematically benchmarks SSA on a suite of 11 functions with different control parameters. We conduct parameter sensitivity analysis of SSA using advanced non-parametric statistical tests to generate statistically significant conclusion on the best performing parameter settings. The conclusion can be adopted in future work to reduce the effort in parameter tuning. In addition, we perform a success rate test to reveal the impact of the control parameters on the convergence speed of the algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/218960
ISBN

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorLi, VOK-
dc.date.accessioned2015-09-18T07:02:16Z-
dc.date.available2015-09-18T07:02:16Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Sendai, Japan, 25-28 May 2015. In Conference Proceedings, 2015, p. 3200-3205-
dc.identifier.isbn978-1-4799-7492-4-
dc.identifier.urihttp://hdl.handle.net/10722/218960-
dc.description.abstractSocial Spider Algorithm (SSA) is a recently proposed general-purpose real-parameter metaheuristic designed to solve global numerical optimization problems. This work systematically benchmarks SSA on a suite of 11 functions with different control parameters. We conduct parameter sensitivity analysis of SSA using advanced non-parametric statistical tests to generate statistically significant conclusion on the best performing parameter settings. The conclusion can be adopted in future work to reduce the effort in parameter tuning. In addition, we perform a success rate test to reveal the impact of the control parameters on the convergence speed of the algorithm.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284-
dc.relation.ispartofCongress on Evolutionary Computation (CEC)-
dc.rightsCongress on Evolutionary Computation (CEC). Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectSocial spider algorithm-
dc.subjectGlobal optimization-
dc.subjectParameter sensitivity analysis-
dc.subjectEvolutionary computation-
dc.subjectMeta-heuristic-
dc.titleParameter sensitivity analysis of social spider algorithm-
dc.typeConference_Paper-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1109/CEC.2015.7257289-
dc.identifier.hkuros254294-
dc.identifier.spage3200-
dc.identifier.epage3205-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151119-

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