A reinforcement learning-based metaheuristic algorithm for solving global optimization problems

dc.authoridAmir Seyyedabbasi / 0000-0001-5186-4499en_US
dc.authorscopusidAmir Seyyedabbasi / 57202833910en_US
dc.authorwosidAmir Seyyedabbasi / HJH-7387-2023en_US
dc.contributor.authorSeyyedabbasi, Amir
dc.date.accessioned2023-03-02T13:49:33Z
dc.date.available2023-03-02T13:49:33Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractThe purpose of this study is to utilize reinforcement learning in order to improve the performance of the Sand Cat Swarm Optimization algorithm (SCSO). In this paper, we propose a novel algorithm for the solution of global optimization problems that is called RLSCSO. In this method, metaheuristic algorithm is combined with rein-forcement learning techniques to form a hybrid metaheuristic algorithm. This study aims to provide search agents with the opportunity to perform efficient exploration of the search space in order to find a global optimal solution by using efficient exploration and exploitation to find optimal solutions within a given search space. A comprehensive evaluation of the RLSCSO has been conducted on 20 benchmark functions and 100-digit chal-lenge basic test functions. Additionally, the proposed algorithm is applied to the problem of localizing mobile sensor nodes, which is NP-hard (nondeterministic polynomial time). Several extensive analyses have been conducted in order to determine the effectiveness and efficiency of the proposed algorithm in solving global optimization problems. In terms of cost values, the RLSCSO algorithm provides the optimal solution, along with tradeoffs between exploration and exploitation.en_US
dc.identifier.citationSeyyedabbasi, A. (2023). A reinforcement learning-based metaheuristic algorithm for solving global optimization problems. Advances in Engineering Software, 178, 103411.en_US
dc.identifier.doi10.1016/j.advengsoft.2023.103411en_US
dc.identifier.issn0965-9978en_US
dc.identifier.issn1873-5339en_US
dc.identifier.scopus2-s2.0-85146646556en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.advengsoft.2023.103411
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3888
dc.identifier.volume178en_US
dc.identifier.wosWOS:000926259500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSeyyedabbasi, Amir
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.subjectMetaheuristic Algorithmen_US
dc.subjectReinforcement Learning Algorithmen_US
dc.subjectSand Cat Swarm Optimizationen_US
dc.subjectQ-Learningen_US
dc.subjectMachine Learningen_US
dc.titleA reinforcement learning-based metaheuristic algorithm for solving global optimization problemsen_US
dc.typeArticleen_US

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