Abstract
The constrained multiobjective optimization problem (CMOP) is common in the real-world applications that need to simultaneously optimize conflicting objectives under certain constraints. In the past two decades, considerable efforts have been devoted to designing constrained multiobjective evolutionary algorithms (CMOEAs) for solving the CMOP. However, as the constraints become more complex, CMOEAs encounter a significant challenge of finding a set of well-distributed Pareto optimal solutions. The infeasible regions caused by the constraints become a major challenge in solving the CMOP since they hinder the population from moving toward the constrained Pareto front. The landscape of infeasible regions plays a significant role in the CMOP but is not well-studied. The present study aims to explore the influences of different landscapes of infeasible regions on the difficulty in solving a CMOP. To this end, we propose an approach to adjusting the landscapes through weights and present the MOEA/D-WSCV that enables the adjustment accordingly. The experimental results show that the inverted generation distance (IGD) and hypervolume (HV) obtained from MOEA/D-WSCV vary with weights, indicating the influences of infeasible regions upon the difficulty in solving CMOPs.