適応度推定を用いた実験ベース進化的多目的最適化の加速 Acceleration of Experiment-Based Evolutionary Multi-Objective Optimization Using Fitness Estimation

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Evolutionary Multi-objective Optimization (EMO) is expected to be a powerful optimization framework for real world problems such as engineering design. Recent progress in automatic control and instrumentation provides us with a smart environment called Hardware In the Loop Simulation (HILS) for experiment-based optimization. However, since conventional technique of Multi-Objective Evolutionary Algorithms (MOEAs) requires a large number of evaluations, it is difficult to apply it to real world problems of costly evaluation. To make Experiment-Based EMO (EBEMO) using the HILS environment feasible, the most important pre-requisite is reduction of the number of necessary fitness evaluations. In the EBEMO, the performance of the evaluation reduction under uncertainty such as observation noise is highly important, although the previous works often assume noise-free environments. In this paper, we propose an evaluation reduction to overcome the above-mentioned problem by selecting the solution candidates by means of the estimated fitness before applying them to the real experiment in MOEAs. This technique is called 'Pre-selection'. For the estimation of fitness, we adopt locally weighted regression. The effectiveness of the proposed method was examined by some numerical experiments and also two-objective four-variable optimization problem of a real internal-combustion engine using HILS.

収録刊行物

  • 電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society  

    電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society 128(3), 388-398, 2008-03-01 

    The Institute of Electrical Engineers of Japan

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各種コード

  • NII論文ID(NAID)
    10021131646
  • NII書誌ID(NCID)
    AN10065950
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    03854221
  • NDL 記事登録ID
    9400978
  • NDL 雑誌分類
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL 請求記号
    Z16-795
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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