自己最良解の分布情報を活用した粒子群分化型Particle Swarm Optimizationによる複数解探索手法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2014/09/01

タイトル(英語): Multiple Optimal Solutions Search by Using Swarm Division Particle Swarm Optimization Algorithm which Utilizes the Distribution of Personal Best Solutions

著者名: 石川　健太（神奈川大学大学院 工学研究科 経営工学専攻），増田　和明（神奈川大学 工学部 情報システム創成学科），瀬古沢　照治（神奈川大学 工学部 情報システム創成学科）

著者名(英語): Kenta Ishikawa (Graduate School of Engineering, Kanagawa University), Kazuaki Masuda (Dept. Information Systems Creation, Faculty of Engineering, Kanagawa University), Teruji Sekozawa (Dept. Information Systems Creation, Faculty of Engineering, Kanagawa University)

キーワード: 粒子群最適化，複数解探索，自己最良解，群分化，引き込み半径推定　　Particle Swarm Optimization (PSO)，Multiple solutions search，Personal-best (pbest)，Swarm division，Estimation of the attracting radius

要約(英語): We propose a new Particle Swarm Optimization (PSO) algorithm which utilizes the information about the distribution of personal bests (pbests). Basically, it applies the standard PSO; however, when the global best (gbest) approaches an optimal solution, its attracting region is estimated by using the distribution information. If particles gather around the gbest, the swarm is divided into two sub-swarms: (a) the local search sub-swarm, which keeps searching for the local solution by using the standard PSO; and, (b) the other solutions search sub-swarm, which moves particles to different solutions by using a modified PSO. When the local search is completed, the standard PSO is appled to all the particles again to trigger the estimation of the attracting region of another optimal solution. Additional resetting of particles in several situations is also applied to keep the diversity of global search. We show the usefulness of the proposed method by numerical experiments.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.134 No.9 （2014） 特集Ⅰ：制御系設計における適応・学習・同定・モデリングの新展開　特集Ⅱ：インテリジェント・システム

本誌掲載ページ: 1373-1383 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/134/9/134_1373/_article/-char/ja/