RBFネットワークと Particle Swarm Optimization による統合的最適化 The Integrative Optimization by RBF Network and Particle Swarm Optimization

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This paper presents a method for the integrative optimization system. Recently, many methods for global optimization have been proposed. The objective of these methods is to find a global minimum of non-convex function. However, large numbers of function evaluations are required, in general. We utilize the response surface method to approximate function space to reduce the function evaluations. The response surface method is constructed from sampling points. The RBF Network, which is one of the neural networks, is utilized to approximate the function space. Then Particle Swarm Optimization (PSO) is applied to the response surface. Proposed system consists of three parts. That is, (Part 1) Generation of the sampling points, (Part 2) Construction of response surface by RBF Network, (Part 3) Optimization by PSO. By iterating these three parts, it is expected that the approximate global minimum of non-convex function can be obtained with a few number of function evaluations. Through numerical examples, the effectiveness and validity are examined.

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(4), 636-645, 2008-04-01 

    The Institute of Electrical Engineers of Japan

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被引用文献:  8件

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各種コード

  • NII論文ID(NAID)
    10021132148
  • 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
    9451525
  • 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  CJP引用  NDL  J-STAGE 
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