Chaotic Sand Cat Swarm Optimization

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dc.authoridNematzadeh, Sajjad/0000-0001-5064-2181
dc.authoridNematzadeh, Sajjad/0000-0001-5064-2181
dc.authoridKiani, Farzad/0000-0002-0354-9344
dc.authoridAFACAN FINDIKLI, MINE M./0000-0003-1021-6641
dc.authorwosidNematzadeh, Sajjad/AAR-1645-2020
dc.authorwosidNematzadeh, Sajjad/HGD-3343-2022
dc.authorwosidKiani, Farzad/O-3363-2013
dc.authorwosidAFACAN FINDIKLI, MINE M./ABB-5962-2020
dc.contributor.authorKiani, Farzad
dc.contributor.authorNematzadeh, Sajjad
dc.contributor.authorAnka, Fateme Aysin
dc.contributor.authorFindikli, Mine Afacan
dc.date.accessioned2024-05-19T14:39:57Z
dc.date.available2024-05-19T14:39:57Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO's core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results.en_US
dc.identifier.doi10.3390/math11102340
dc.identifier.issn2227-7390
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85160528238en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.3390/math11102340
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4880
dc.identifier.volume11en_US
dc.identifier.wosWOS:000996982100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofMathematicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectChaotic Sand Cat Swarm Optimizationen_US
dc.subjectChaotic Mapsen_US
dc.subjectConstrained Problemsen_US
dc.subjectMultidisciplinary Problemsen_US
dc.subjectHybrid Metaheuristicsen_US
dc.titleChaotic Sand Cat Swarm Optimizationen_US
dc.typeArticleen_US

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