期待改善量と一般化包絡分析法を用いたパレート最適解の生成法

抄録

Evolutionary optimization methods, for example, genetic algorithms and particle swarm optimization have been applied for solving multi-objective optimization problems, and have been observed to be useful for generating Pareto optimal solutions. In order to generate good approximate and well-distributed Pareto optimal solutions with a small number of function evaluations, this paper suggests a new recombination method by utilizing expected improvement and generalized data envelopment analysis in a real-coded genetic algorithm. In addition, the effectiveness of the proposed method will be investigated through several numerical examples, by comparison with the conventional methods.