Robust Optimization by ε-Ranking on High Dimensional Objective Spaces
This work proposes a method to fine grain the ranking of solutions after they have been ranked by Pareto dominance, aiming to improve the performance of evolutionary algorithms on many objectives optimization problems. The re-ranking method uses a randomized sampling procedure to choose, from sets of equally ranked solutions, those solutions that will be given selective advantage. The sampling procedure favors a good distribution of the sampled solutions based on dominance regions wider than conventional Pareto dominance. We enhance NSGA-II with the proposed method and test its performance on 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 objectives problems.
KeywordsMultiobjective Optimization Pareto Optimal Solution Epistatic Interaction Objective Space Robust Optimization
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