設計工学・システム部門講演会講演論文集
Online ISSN : 2424-3078
セッションID: 3403
会議情報
3403 計算知能による目的関数予測にもとづく最適化(OS03/近似最適化)
中山 弘隆荒川 雅生佐々木 理恵
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会議録・要旨集 フリー

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抄録
In many practical engineering design problems, the form of objective function is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the value of objective function is obtained by some analysis such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. Usually, these analyses are considerably time consuming to obtain a value of objective function. In order to make the number of analyses as few as possible, we suggest a method by which optimization is performed in parallel with predicting the form of objective function. Techniques of machine learning can be applied for predicting the form of objective function. In this paper, radial basis function networks (RBFN) are employed in predicting the form of objective function, and genetic algorithms (GA) in searching the optimal value of the predicted objective function. The effectiveness of the suggested method will be shown through some numerical examples.
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© 2001 一般社団法人 日本機械学会
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