Differential evolution with individual and correlation information utilization for constrained optimization problems

Abstract

Compared to unconstrained global optimization, constrained optimization problems (COPs) introduce extra complexity to the search space due to constraints, making the issues more challenging to solve. COPs necessitate an algorithm with enhanced exploration and exploitation capabilities, as well as a constraint handling technique (CHT) that can be easily integrated and effectively balance constraints and objectives. To fully leverage evolutionary information in solving COPs, this paper proposes a constrained differential evolution algorithm (called IUCDE) based on the utilization of individual and correlation information. The constraints, objectives, and distances of individuals in the population are integrated into individual information, which is represented by individual performance scores. Based on the individual information, individuals are categorized into three subpopulations with distinct search characteristics to maximize their potential. Based on the correlation information generated in each iteration of the population, a dynamic feasibility rule is proposed, which, combined with the original feasibility rule, is adaptively selected to handle constraints based on the proportion of feasible solutions in the population. The proposed IUCDE algorithm is compared with five state-of-the-art constrained optimization algorithms across 22 test problems from the CEC 2017 benchmark, demonstrating superior performance. Furthermore, IUCDE exhibits a competitive advantage in solving 41 test problems from the CEC 2020 real-world constrained optimization test benchmark. Extensive experiments have validated the efficient execution of IUCDE and the effectiveness of its components.