A binary social spider algorithm for continuous optimization task

Basit Öğe Kaydı
dc.contributor.authorBas, Emine
dc.contributor.authorUlker, Erkan
dc.date.accessioned2020-03-26T20:12:14Z
dc.date.available2020-03-26T20:12:14Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of SSA (binary SSA) is introduced to solve binary problems. Currently, there is insufficient focus on the binary version of SSA in the literature. The main part of the binary version is at the transfer function. The transfer function is responsible for mapping continuous search space to discrete search space. In this study, four of the transfer functions divided into two families, S-shaped and V-shaped, are evaluated. Thus, four different variations of binary SSA are formed as binary SSA-Tanh, binary SSA-Sigm, binary SSA-MSigm and binary SSA-Arctan. Two different techniques (SimSSA and LogicSSA) are developed at the candidate solution production schema in binary SSA. SimSSA is used to measure similarities between two binary solutions. With SimSSA, binary SSA's ability to discover new points in search space has been increased. Thus, binary SSA is able to find global optimum instead of local optimums. LogicSSA which is inspired by the logic gates and a popular method in recent years has been used to avoid local minima traps. By these two techniques, the exploration and exploitation capabilities of binary SSA in the binary search space are improved. Eighteen unimodal and multimodal standard benchmark optimization functions are employed to evaluate variations of binary SSA. To select the best variations of binary SSA, a comparative study is presented. The Wilcoxon signed-rank test has applied to the experimental results of variations of binary SSA. Compared to well-known evolutionary and recently developed methods in the literature, the variations of binary SSA performance is quite good. In particular, binary SSA-Tanh and binary SSA-Arctan variations of binary SSA showed superior performance.en_US
dc.identifier.doi10.1007/s00500-020-04718-wen_US
dc.identifier.issn1432-7643en_US
dc.identifier.issn1433-7479en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00500-020-04718-w
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37387
dc.identifier.wosWOS:000510297200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofSOFT COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBinary optimizationen_US
dc.subjectSocial spider algorithmen_US
dc.subjectTransfer functionen_US
dc.titleA binary social spider algorithm for continuous optimization tasken_US
dc.typeArticleen_US

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