PSCSO: enhanced sand cat swarm optimization inspired by the political system to solve complex problems

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dc.authoridFahri Erenel / 0000-0001-8943-7265en_US
dc.authoridFateme Ayşin Anka / 0000-0002-2795-6438
dc.authorscopusidFahri Erenel / 55643879500en_US
dc.authorscopusidFateme Ayşin Anka / 57414861500
dc.authorwosidFateme Ayşin Anka / GNA-1067-2022en_US
dc.authorwosidErenel, Fahri / G-5204-2019en_US
dc.contributor.authorKiani, Farzad
dc.contributor.authorAnka, Fateme Ayşin
dc.contributor.authorErenel, Fahri
dc.date.accessioned2023-05-22T12:04:12Z
dc.date.available2023-05-22T12:04:12Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Siyaset Bilimi ve Kamu Yönetimi Bölümüen_US
dc.description.abstractThe Sand Cat Swarm Optimization (SCSO) algorithm is a recently introduced metaheuristic with balanced behavior in the exploration and exploitation phases. However, it is not fast in convergence and may not be successful in finding the global optima, especially for complex problems since it starts the exploitation phase late. Moreover, the performance of SCSO is also affected by incorrect position as it depends on the location of the global optimum. Therefore, this study proposes a new method for the SCSO algorithm with a multidisciplinary principle inspired by the Political (Parliamentary) system, which is named PSCSO. The suggested algorithm increases the chances of finding the global solution by randomly choosing positions between the position of the candidate's best solution available so far and the current position during the exploitation phase. In this regard, a new coefficient is defined that affects the exploration and exploitation phases. In addition, a new mathematical model is introduced to use in the exploitation phase. The performance of the PSCSO algorithm is analyzed on a total of 41 benchmark functions from CEC2015, 2017, and 2019. In addition, its performance is evaluated in four classical engineering problems. The proposed algorithm is compared with the SCSO, Stochastic variation and Elite collaboration in SCSO (SE-SCSO), Hybrid SCSO (HSCSO), Parliamentary Optimization Algorithm (POA), and Arithmetic Optimization Algorithm (AOA) algorithms, which have been proposed in recent years. The ob-tained results depict that the PSCSO algorithm performs better or equivalently to the compared optimization algorithms.en_US
dc.identifier.citationKiani, F., Anka, F. A., & Erenel, F. (2023). PSCSO: Enhanced sand cat swarm optimization inspired by the political system to solve complex problems. Advances in Engineering Software, 178, 103423.en_US
dc.identifier.doi10.1016/j.advengsoft.2023.103423en_US
dc.identifier.issn0965-9978en_US
dc.identifier.issn1873-5339en_US
dc.identifier.scopus2-s2.0-85146647821en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.advengsoft.2023.103423
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3912
dc.identifier.volume178en_US
dc.identifier.wosWOS:000926264400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorAnka, Fateme Ayşin
dc.institutionauthorErenel, Fahri
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofAdvances in Engineering Softwareen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOptimizationen_US
dc.subjectMetaheuristicsen_US
dc.subjectParliamentary Systemen_US
dc.subjectSand Cat Swarm Optimizationen_US
dc.titlePSCSO: enhanced sand cat swarm optimization inspired by the political system to solve complex problemsen_US
dc.typeArticleen_US

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