Multi-population Constrained Multi-objective Evolutionary Algorithm Based on Knowledge Transfer

Constrained multi-objective problems face the challenge of simultaneously optimizing the objective functions and constraint satisfaction. The difficulty in addressing this challenge lies in considering convergence, feasibility, and diversity simultaneously. To better solve CMOPs, this paper proposes a multi-population constrained multi-objective evolutionary algorithm based on knowledge transfer (C-MTEA). It consists of three different populations, i.e., the main population, the archive population, and the auxiliary population, that can cooperate with each other to evolve collectively. Specifically, the main population and the archive population cooperate by utilizing different search strategies to generate complementary offspring, while the auxiliary population, which does not consider constraints, can assist the main population in convergence. In the environmental selection stage, useful information is transferred across populations by sharing offspring generated by various strategies, thus facilitating the evolution of populations. To validate the effectiveness of the proposed C-MTEA, experiments are carried out on 5 popular benchmark suites containing up to 63 instances. The results demonstrate that the proposed algorithm is competitive with state-of-the-art constrained multi-objective optimization evolutionary algorithms (CMOEAs).