LAUSR

Abstract The feasibility requirement of constraints poses a severe obstacle to the algorithms’ convergence and diversity when solving constrained multi-objective optimization problems (CMOPs) in science and engineering. This paper adopts a stricter constraint dominance principle, called SCDP, no longer only prioritizing the feasibility optimization while enhancing algorithmic conflicting multi-performance, such as convergence, diversity, and feasibility simultaneously. Firstly, the approach identifies the non-dominated constraints closest to the constrained Pareto Front (CPF) within the feasible regions. Subsequently, convergence, diversity, and feasibility are quantified as competing objectives that stricter the constraint dominance principle (CDP) to optimize multiple performances under non-dominated constraints. The effectiveness of the proposed SCDP is validated through the evaluation of 32 constrained multi-objective problems (CMOPs) and practical applications in the CMOP domain. The results demonstrate that the SCDP based algorithm can improve all the conflict multi-performance of the final solutions when solving CMOPs.