適応DEにおける確率的なパラメータ調整法とその評価

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: 2015/09/01

タイトル(英語): Introducing a Stochastic Parameter Control Method to an Adaptive Differential Evolution

著者名: 門田　真樹（広島大学大学院工学研究科），保田　俊行（広島大学大学院工学研究科），松村　嘉之（信州大学繊維学部），大倉　和博（広島大学大学院工学研究科）

著者名(英語): Masaki Kadota (Graduate School of Engineering, Hiroshima University), Toshiyuki Yasuda (Graduate School of Engineering, Hiroshima University), Yoshiyuki Matsumura (Faculty of Textile Science and Technology, Shinshu University), Kazuhiro Ohkura (Graduate School of Engineering, Hiroshima University)

キーワード: Differential Evolution，実数値関数最適化　　Differential Evolution，Numerical Optimization

要約(英語): Differential Evolution (DE) is a population-based stochastic search method for real-valued function optimization. Like other metaheuristic algorithms, DE finds optimal or near-optimal solutions without a priori knowledge about the function being optimized. However, DE generally shows largely different performance according to the DE parameters adopted. Therefore, various DE variants have been developed in order to obtain more stable and better performance. A DE variant called SHADE is one of the highly competitive DE variants so far. SHADE introduces parameter archives for parameter adaptation to generate better optimization results. In this paper, SHADE is extended in such a way that parameter archives are managed by novel three strategies so that DE parameters are robust against fixation which may occur by trapping the evolutionary search into local optima. We call this method the robust SHADE, i.e., RSHADE. The computer simulations are conducted to examine the performance of RSHADE on 28 benchmarks.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.9 （2015） 特集：サステナブル社会・先端応用へ向けたレーザプロセシング技術

本誌掲載ページ: 1142-1148 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/135/9/135_1142/_article/-char/ja/