Extending Pareto Dominance for Multi-Constraints Satisfaction and Multi-Performance Enhancement in Constrained Multi-Objective Optimization

Multi-objective optimization problems (MOPs) in science and engineering frequently involve intricate multi-constraints. This paper extends the application of the Pareto dominance in MOPs on addressing complex multi-constraints and enhancing algorithmic conflicting multi-performance, such as convergence, diversity, and feasibility. The approach begins by identifying non-dominated constraints that closest approximate the actual constrained Pareto Front (CPF) through Pareto non-dominated sorting of every single constrained Pareto Front (SCPF). Subsequently, a Pareto non-dominated sorting multi-performance methodology is employed under the determined non-dominated constraints, considering convergence, diversity, and feasibility as competing objectives. Building upon extending the Pareto dominance approach for constrained multi-objective optimization (EPDCMO), this paper introduces a dual-population multi-archive optimization mechanism to optimize multiple constraints and performance simultaneously. The effectiveness of the proposed approach is validated through the evaluation of 23 constrained multi-objective problems (CMOPs) and practical applications in the domain of CMOPs. The results demonstrate the algorithm's capability to generate competitive solutions for MOPs characterized by multi-constraints.