A study on the effects of rankings sensitive to density on many-objective MNK Landscapes


This work investigates e-ranking and e-box non-domination sorting, two methods that incorporate e-dominance concepts to estimate density of solutions and control the number of rank-1 solution for many-objective optimization. We study how convergence and spread of solutions are affected by rankings that are based on local information of the distribution of solutions without considering closeness-to-dominance information. We also study the robustness of the methods to parameters settings, and how the methods react when extreme solutions are enforced or not. MNK-Landscapes are used as test problems in our study.