214 パレート解集合の精度と幅広さを考慮する多目的遺伝的アルゴリズムの探索戦略

概要

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In multi-objective optimization, it is important to derive solutions with good accuracy, uniform distribution, and broadness. Of these qualities, we focused on accuracy and broadness of the solutions and proposed a search strategy to improve them. Since it is difficult to improve both convergence and broadness of the solutions at the same time in a multi-objective GA search, we considered to converge the solutions first and then broaden them in the proposed search strategy by dividing the search into two search phases. The first phase is to improve convergence of the solutions, and a reference point specified by a decision maker is adopted in this search. In the second phase, the solutions are broadened using the Distributed Cooperation Scheme. From the results of the numerical experiment, we found that the proposed search strategy is capable of deriving broader solutions than conventional multi-objective GA without deterioration of accuracy.
一般社団法人日本機械学会の論文
2008-11-26