Behavior Analysis of Constrained Multiobjective Evolutionary Algorithms using Scalable Constrained Multi-Modal Distance Minimization Problems

This paper proposes scalable constrained multimodal distance minimization problems to evaluate algorithm behaviors against a multi-modal property and constraints that often appear in real-world optimization problems. Our previous study proposed two-dimensional constrained multi-modal distance minimization problems (CMDMPs), which include the above characteristics. This paper extends CMDMPs to scalable problems which can define any number of decision variables. They can be used to examine the effects of the number of decision variables on the search performance of constrained multiobjective evolutionary algorithms (MOEAs). In computational experiments, we evaluate two MOEAs, i.e., NSGA-II and DNEA, and three constraint handling methods, i.e., CDP, IEpsilon, and SP, using the proposed CMDMPs.