タカハマ　セツコ   TAKAHAMA Setsuko   高濱　節子    所属   広島修道大学  商学部    職種   教授
言語種別	英語
発行・発表の年月	2022/02
形態種別	著書
標題	A Study on Multi-Armed Bandit Algorithms for Dynamic Selection of Parameters and Topologies in Particle Swarm Optimization
執筆形態	分担
掲載誌名	Operations Researche and Information Systems
掲載区分	国内
出版社・発行元	Kyushu University Press
巻・号・頁	pp.21-48
頁数	28
著者・共著者	Setsuko Sakai and Tetsuyuki Takahama
概要	The multi-armed bandit problem is defined as maximizing the total reward when the reward of each choice is unknown and a choice is sequentially selected from multiple choices. Particle Swarm Optimization (PSO) has been successfully applied to various optimization problems. Various parameter (including topology) settings are known for PSO, but it is difficult to determine the appropriate setting because the setting depends on the problem to be solved and the search process. In this study, we propose to apply bandit algorithms to the parameter setting. If a new position after a movement is better than the personal best position found so far, the reward is 1 as success, otherwise the reward is 0 as failure. The setting that maximizes the cumulative reward is discovered by the bandit algorithms. The effectiveness of the proposed method is shown by introducing the method to PSO and optimizing 13 benchmark problems.
ISBN	9784798503264