A dual-population and three-stage constrained multi-objective evolutionary algorithm based on alternative evolution and degeneration

Constrained multi-objective optimization problems (CMOPs) are challenging due to conflicting objectives and numerous constraints. In recent years, dual-population constrained multi-objective evolutionary algorithms (DP-CMOEAs) have been shown impressive performance in solving CMOPs, in which the main population searches for the constrained Pareto front (CPF), while the auxiliary population focuses on locating the unconstrained Pareto front (UPF) and providing valuable information to the main population. However, for existing DP-CMOEAs, the way of utilizing the auxiliary population is either under-utilized or over-utilized, leaving room for further performance improvement. To tackle this, this paper proposes a novel DP-CMOEA based on alternative evolution and degeneration. The proposed algorithm utilizes the auxiliary population by alternating between evolutionary and degradation stages based on its state. In the third stage, the auxiliary population ceases to evolve, thus avoiding unnecessary computational resource waste. Experimental results on three benchmark suites and three real-world problems show that the proposed algorithm outperforms seven state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) in terms of both convergence and diversity.