Abstract
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.
