A Cooperative Dual Population Evolutionary Algorithm with Additional Constraint Violation Degree

Solving constrained multi-objective optimization (CMOPs) poses a significant challenge due to the need to balance multiple interconnected objectives and constraint values. While numerous evolutionary algorithms have been developed to address such problems, they often suffer from low efficiency when dealing with CMOPs. To overcome these challenges, this paper introduces a novel algorithm called DPMOEA (Dual-Population Multi-Objective Evolutionary Algorithm). DPMOEA incorporates dual-population and cooperative characteristics. The main population is employed to address the original CMOP problem, while the auxiliary population focuses on optimizing an M+1 problem comprising of M objective values and constraint violation degrees. Both populations explore the global and feasible solution spaces independently. By exchanging solutions between them, the auxiliary population assists the main population in avoiding local optima and effectively approaching the Pareto Front (PF). To validate the effectiveness of the proposed algorithm, extensive experiments are conducted on three well-known test suites. The results demonstrate the superior performance of DPMOEA when compared to five state-of-the-art Constrained Multi-Objective Evolutionary Algorithms (CMOEA).