3124 RBFネットワークを用いた逐次近似最適化 : サンプリング関数の提案(J03-1 解析・設計の高度化・最適化(1),J03 解析・設計の高度化・最適化)

概要

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Sequential Approximate Optimization (SAO) is one of the most attractive research areas in the design optimization. The Radial Basis Function Network (RBFN) is used to construct the response surface. One of the important aspects in the SAO is the sampling strategy. This paper proposes the new method for the sampling strategy. The sampling function to add a new sample point is proposed. The local or global minima of the sampling function correspond to the sparse region of the sample points. To minimize the sampling function, a new sample point is found. The Particle Swarm Optimization (PSO) is used to minimize the sampling function. Through the numerical examples, the validity of proposed method is examined.
2007-09-07