A Constrained Multi-Objective Optimization Algorithm with a Population State Discrimination Model

The solution to constrained multi-objective optimization problems (CMOPs) requires optimizing the objective functions while satisfying the constraint conditions. To effectively address CMOPs, algorithms must balance objectives and constraints. However, the limited adaptability of specific constraint-handling techniques (CHTs) has hindered the widespread applicability of constrained multi-objective evolutionary algorithms (CMOEAs). To overcome this limitation, this article proposes a population state-based CMOEA. First, a model is developed to identify population states based on the positions of the primary and auxiliary populations. Tailored environmental selection models are then designed for the auxiliary population according to different states, enabling them to guide the evolution of the main population more effectively. By dynamizing the CHTs, the proposed algorithm can adapt to a broader and more complex range of CMOPs. Additionally, state-specific optimal individual selection methods are introduced, allowing the auxiliary population to escape local optima and accelerate exploration. A simple yet effective resource allocation model is incorporated to address the potential computational resource waste associated with dual populations, enhancing the resource utilization. Comprehensive tests, including comparisons with seven state-of-the-art algorithms, were conducted on 47 benchmark functions and 12 real-world problems. The experimental results demonstrate that the proposed CMOEA outperforms existing CMOEAs in its convergence and diversity.