Robust Optimization by epsilon-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 oil many objective's 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 with up to M = 10 objectives. Experimental result's show that, convergence and diversity of the solutions found call improve remarkably oil 3 <= M <= 10 objectives problems.