2C2-2 目的関数間の関連度合いの違いによる進化型多目的最適化アルゴリズムの探索性能への影響(最適化・進化計算)

概要

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Recently it has been pointed out in many studies that evolutionary multi-objective optimization (EMO) algorithms with Pareto dominance-based fitness evaluation do not work well on many-objective problems with four or more objectives. In this paper, we examine the behavior of well-known and frequently-used EMO algorithms such as NSGA-II, SPEA2, and MOEA/D on many-objective problems with correlated or dependent objectives. First, we show that the search ability of NSGA-II and SPEA2 is not degraded by the increase in the number of objectives when they are highly correlated or dependent. Next, we use the different degrees of correlation among correlated or dependent objectives. We show that the increase in the number of objectives has a different effect on the behavior of an EMO algorithm depending on the relation among objectives, the degree of correlation, and the characteristics of the used EMO algorithm.
一般社団法人日本機械学会の論文
2011-09-01