PSOとDEによるハイブリッド手法の計算特性

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: 2012/07/01

タイトル(英語): Computational Property of Hybrid Methods with PSO and DE

著者名: 村中　健一（慶應義塾大学 大学院理工学研究科），相吉　英太郎（慶應義塾大学 大学院理工学研究科）

著者名(英語): Kenichi Muranaka (Graduate School of Science and Technology, Keio University), Eitaro Aiyoshi (Graduate School of Science and Technology, Keio University)

キーワード: 大域的探索，メタヒューリスティクス，粒子群最適化，差分進化，ハイブリッド手法　　global search，meta-heuristics，particle swarm optimization，differential evolution，hybrid method

要約(英語): In this paper, we present a new type of hybrid methods for global optimization with Particle Swarm Optimization (PSO) and Differential Evolution (DE), which have attracted interests as heuristic and global optimization methods recently. Concretely, "p-best solutions" as the targets of PSO's particles are actuated by DE's evolutional mechanism in order to promote PSO's global searching ability. The presented hybrid method works effectively because PSO acts as a local optimizer and DE plays a role as a global optimizer. To evaluate performance of the hybridization, our method is applied to some benchmarks and is compared with the separated PSO and DE. Through computer simulations, it is certified that the proposed hybrid method performs fairy better than their separated algorithm.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.132 No.7 （2012） 特集：平成23年電気学会電子・情報・システム部門大会

本誌掲載ページ: 1128-1135 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/132/7/132_1128/_article/-char/ja/