A strengthened constrained-dominance based evolutionary algorithm for constrained many-objective optimization

Abstract

Solving constrained multi-objective optimization problems have received increasing attention. However, there are few researches based on constrained many-objective optimization problems that widely exist in real life. Given the above fact, we propose a strengthened constrained-dominance based evolutionary algorithm for constrained many-objective optimization (SCEA). The proposed SCEA includes the following main components. First, a dual-assistance mating selection is developed to select elite parents for variation, and further accelerate the generation of feasible solutions. Second, a strengthened constrained-dominance relation is proposed, which favors feasible solutions but still leaves the room for selecting infeasible solutions. This is achieved by simultaneously considering the objective optimization and constraint satisfaction. Third, the designed unconstrained aggregation (UA) indicator and crowded detector cooperate reference points to promote the convergence and diversity of population. Finally, a cooperation mechanism based on the constrained aggregation (CA) indicator and hierarchical clustering is designed to drive individuals toward different feasible regions, and further balance the objective optimization and constraint satisfaction. Extensive experimental studies are conducted on three benchmark test suites and two real-world applications to validate the performance of SCEA. The corresponding experiment results have demonstrated that SCEA is more competitive than its peer competitors.