Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems

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dc.authoridAmir Seyyedabbasi / 0000-0001-5186-4499en_US
dc.authoridFarzad Kiani / 0000-0002-0354-9344en_US
dc.authorscopusidFarzad Kiani / 36662461100
dc.authorscopusidAmir Seyyedabbasi / 57202833910
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023en_US
dc.authorwosidFarzad Kiani / O-3363-2013en_US
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorKiani, Farzad
dc.date.accessioned2022-05-26T09:24:29Z
dc.date.available2022-05-26T09:24:29Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractThis study proposes a new metaheuristic algorithm called sand cat swarm optimization (SCSO) which mimics the sand cat behavior that tries to survive in nature. These cats are able to detect low frequencies below 2 kHz and also have an incredible ability to dig for prey. The proposed algorithm, inspired by these two features, consists of two main phases (search and attack). This algorithm controls the transitions in the exploration and exploitation phases in a balanced manner and performed well in finding good solutions with fewer parameters and operations. It is carried out by finding the direction and speed of the appropriate movements with the defined adaptive strategy. The SCSO algorithm is tested with 20 well-known along with modern 10 complex test functions of CEC2019 benchmark functions and the obtained results are also compared with famous metaheuristic algorithms. According to the results, the algorithm that found the best solution in 63.3% of the test functions is SCSO. Moreover, the SCSO algorithm is applied to seven challenging engineering design problems such as welded beam design, tension/compression spring design, pressure vessel design, piston lever, speed reducer design, three-bar truss design, and cantilever beam design. The obtained results show that the SCSO performs successfully on convergence rate and in locating all or most of the local/global optima and outperforms other compared methods.en_US
dc.identifier.citationSeyyedabbasi, A., Kiani, F. (2022). Sand cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers.en_US
dc.identifier.doi10.1007/s00366-022-01604-xen_US
dc.identifier.issn0177-0667en_US
dc.identifier.scopus2-s2.0-85128019154en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s00366-022-01604-x
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2749
dc.identifier.wosWOS:000780842800001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSeyyedabbasi, Amir
dc.institutionauthorKiani, Farzad
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofENGINEERING WITH COMPUTERSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMetaheuristicsen_US
dc.subjectSand Cat Swarm Optimizationen_US
dc.subjectSwarm Intelligenceen_US
dc.subjectOptimizationen_US
dc.titleSand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problemsen_US
dc.typeArticleen_US

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