File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A social spider algorithm for global optimization

TitleA social spider algorithm for global optimization
Authors
Issue Date2015
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc
Citation
Applied Soft Computing, 2015, v. 30, p. 614-627 How to Cite?
AbstractThe growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel social spider algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The social spider algorithm is evaluated by a series of widely used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.
Persistent Identifierhttp://hdl.handle.net/10722/217035
ISSN
2015 Impact Factor: 2.857
2015 SCImago Journal Rankings: 1.763

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorLi, VOK-
dc.date.accessioned2015-09-18T05:46:37Z-
dc.date.available2015-09-18T05:46:37Z-
dc.date.issued2015-
dc.identifier.citationApplied Soft Computing, 2015, v. 30, p. 614-627-
dc.identifier.issn1568-4946-
dc.identifier.urihttp://hdl.handle.net/10722/217035-
dc.description.abstractThe growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Metaheuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Inspired by the social spiders, we propose a novel social spider algorithm to solve global optimization problems. This algorithm is mainly based on the foraging strategy of social spiders, utilizing the vibrations on the spider web to determine the positions of preys. Different from the previously proposed swarm intelligence algorithms, we introduce a new social animal foraging strategy model to solve optimization problems. In addition, we perform preliminary parameter sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values. The social spider algorithm is evaluated by a series of widely used benchmark functions, and our proposed algorithm has superior performance compared with other state-of-the-art metaheuristics.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/asoc-
dc.relation.ispartofApplied Soft Computing-
dc.rightsPosting accepted manuscript (postprint): © <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleA social spider algorithm for global optimization-
dc.typeArticle-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.asoc.2015.02.014-
dc.identifier.hkuros254272-
dc.identifier.volume30-
dc.identifier.spage614-
dc.identifier.epage627-
dc.publisher.placeNetherlands-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats