Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems

Basit Öğe Kaydı
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorAliyev, Royal
dc.contributor.authorKiani, Farzad
dc.contributor.authorGulle, Murat Ugur
dc.contributor.authorBasyildiz, Hasan
dc.contributor.authorShah, Mohammed Ahmed
dc.date.accessioned2024-03-13T10:35:02Z
dc.date.available2024-03-13T10:35:02Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThis paper introduces three hybrid algorithms that help in solving global optimization problems using reinforcement learning along with metaheuristic methods. Using the algorithms presented, the search agents try to find a global optimum avoiding the local optima trap. Compared to the classical metaheuristic approaches, the proposed algorithms display higher success in finding new areas as well as exhibiting a more balanced performance while in the exploration and exploitation phases. The algorithms employ reinforcement agents to select an environment based on predefined actions and tasks. A reward and penalty system is used by the agents to discover the environment, done dynamically without following a predetermined model or method. The study makes use of Q-Learning method in all three metaheuristic algorithms, so-called RLI-GWO, RLEx-GWO, and RLWOA algorithms, so as to check and control exploration and exploitation with Q-Table. The Q-Table values guide the search agents of the metaheuristic algorithms to select between the exploration and exploitation phases. A control mechanism is used to get the reward and penalty values for each action. The algorithms presented in this paper are simulated over 30 benchmark functions from CEC 2014, 2015 and the results obtained are compared with well-known metaheuristic and hybrid algorithms (GWO, RLGWO, I-GWO, Ex-GWO, and WOA). The proposed methods have also been applied to the inverse kinematics of the robot arms problem. The results of the used algorithms demonstrate that RLWOA provides better solutions for relevant problems. (C) 2021 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.knosys.2021.107044
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85104926933en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2021.107044
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4223
dc.identifier.volume223en_US
dc.identifier.wosWOS:000651271700008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_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.subjectGrey wolf optimization algorithmen_US
dc.subjectWhale optimization algorithmen_US
dc.subjectQ-learningen_US
dc.titleHybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problemsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu