Cooperative Multiobjective Evolutionary Algorithm With Propulsive Population for Constrained Multiobjective Optimization

Convergence, diversity and feasibility are three important issues when solving constrained multiobjective optimization problems (CMOPs). To deal with the balance among convergence, diversity and feasibility well, this article proposes a cooperative multiobjective evolutionary algorithm with propulsive population (CMOEA-PP) for solving CMOPs. CMOEA-PP has two populations, including propulsive population and normal population, and these two populations work cooperatively. Specifically, propulsive population focuses on convergence. Normal population gives priority to feasibility and is obligated to maintain diversity. To cross through the infeasible region and reach the Pareto front (PF), propulsive population does not consider constraints in the early stage and only considers constraints in the later stage. To further accelerate the speed of convergence, propulsive population only searches for corner solutions and center solutions, while normal population searches for the whole PF. As a result, propulsive population can cross through the infeasible region because of the lack of attention to feasibility. In addition, propulsive population also can guide and accelerate the convergence of the evolutionary process. Comprehensive experiment results on several sets of benchmark problems demonstrate that CMOEA-PP is better than existing state-of-the-art competitors.

Automated summary

This article proposes a cooperative multiobjective evolutionary algorithm with propulsive population (CMOEA-PP) to solve constrained multiobjective optimization problems (CMOPs), balancing convergence, diversity, and feasibility through the collaboration between propulsive population and normal population. Propulsive population focuses on convergence, while normal population prioritizes feasibility and must maintain diversity.