A Multicriteria Game-Driven Constrained Multiobjective Evolutionary Algorithm

Solving constrained multi-objective optimization problems (CMOPs) involves simultaneously achieving convergence to the Pareto front, preserving solution diversity, and satisfying complex constraints. To address these challenges, this paper proposes a Multi-Criteria Game-driven Constrained Multi-Objective Evolutionary Algorithm (MGCMOEA), which leverages the complementary roles of a main population and an auxiliary population to jointly explore both feasible and infeasible regions. The algorithm introduces a refined feasibility classification mechanism that differentiates weakly feasible solutions from infeasible ones, thereby accelerating convergence while preserving potentially valuable solutions. To enhance cooperation between populations, MGCMOEA incorporates a multi-criteria game-theoretic selection model that integrates dynamic game theory with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This model improves the guidance provided by the auxiliary population during evolution. Furthermore, to overcome inefficiencies associated with static population sizes, a dynamic resource allocation strategy is adopted, enabling adaptive distribution of computational resources throughout the evolutionary process. Experimental results on 47 benchmark functions and 12 real-world CMOPs demonstrate that MGCMOEA consistently outperforms 8 state-of-the-art algorithms in terms of convergence, diversity, and feasibility.