ε-Ranking for Effective Many Objective Optimization on MNK-Landscapes

概要

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This work proposes a method to enhance selection of multiobjective evolutionary algorithms aiming to improve their performance on many objective optimization problems. The proposed method uses a randomized sampling procedure combined with ε-dominance to fine grain the ranking of solutions after they have been ranked by Pareto dominance. The sampling procedure chooses a subset of initially equal ranked solutions to give them selective advantage, favoring a good distribution of the sample based on dominance regions wider than conventional Pareto dominance. We enhance NSGA-II with the proposed method and analyze its performance on a wide range of non-linear problems using MNK-Landscapes with up to M = 10 objectives. Experimental results show that convergence and diversity of the solutions found can improve remarkably on 3≦M≦10 objective problems.
一般社団法人情報処理学会の論文
2009-12-11