ノイズを含む多目的最適化問題に対する最悪状況の予測に基づく差分進化の適用

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: 2016/02/01

タイトル(英語): Worst Case Prediction-based Differential Evolution For Multi-Noisy-Hard-objective Optimization Problems

著者名: 田川　聖治（近畿大学理工学部），原田　翔一（近畿大学大学院総合理工学研究科）

著者名(英語): Kiyoharu Tagawa (School of Science and Engineering, Kinki University), Shoichi Harada (Graduate School of Science and Engineering Research, Kinki University)

キーワード: ノイズを含む関数最適化，進化型多目的最適化，差分進化，予測区間　　noisy-function optimization，evolutionary multi-objective optimization，differential evolution，prediction interval

要約(英語): A new multi-objective optimization problem in presence of noise is formulated and called Multi-Noisy-Hard-objective Optimization Problem (MNHOP). Since considering the worst case performance is important in many real-world optimization problems, each solution of MNHOP is evaluated based on the upper bounds of noisy objective functions' values predicted statistically from multiple samples. Then an Evolutionary Multi-objective Optimization Algorithm (EMOA) based on Differential Evolution is applied to MNHOP. Three sample saving techniques, namely U-cut, C-cut, and re-sampling, are proposed and introduced into the EMOA for allocating its computing budget only to promising solutions. Finally, the effects of those techniques are examined through numerical experiments.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.136 No.2 （2016）

本誌掲載ページ: 189-198 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/136/2/136_189/_article/-char/ja/