A reinforcement learning-based metaheuristic algorithm for solving global optimization problems
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Metaheuristic Algorithm, Reinforcement Learning Algorithm, Sand Cat Swarm Optimization, Q-Learning, Machine Learning
Kaynak
Advances In Engineering Software
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
178
Sayı
Künye
Seyyedabbasi, A. (2023). A reinforcement learning-based metaheuristic algorithm for solving global optimization problems. Advances in Engineering Software, 178, 103411.
