An Instance Space Analysis of Constrained Multiobjective Optimization Problems

Abstract

Constrained multiobjective optimization problems (CMOPs) are generally more challenging than unconstrained problems. This in part can be attributed to the infeasible region generated by the constraint functions, the interaction between constraints and objectives, or both. In this article, we explore the relationship between the performance of constrained multiobjective evolutionary algorithms (CMOEAs) and the instance characteristics of CMOP using instance space analysis (ISA). To do this, we extend recent work on Landscape Analysis features for characterizing CMOPs. Specifically, we introduce new features to describe the multiobjective-violation landscape, formed by the interaction between ..