Adaptive information fusion–driven evolutionary algorithm via balancing the information from unconstrained and constrained pareto fronts

Abstract

Obtaining well–converged and well–distributed constrained Pareto fronts (CPFs) is the ultimate goal of solving constrained multi–objective optimization problems (CMOPs). In recent years, leveraging information from the unconstrained Pareto front (UPF) has become a prevalent method for CMOPs. However, the equilibrium and representation of information from CPF and UPF are crucial to the performance of evolutionary algorithms. To balance the information from CPF and UPF adaptively, this paper proposes an adaptive information fusion–driven evolutionary algorithm, referred to as AIFDEA. Specifically, the evolutionary process of AIFDEA is divided into infeasible and feasible stages. During the infeasible stage, a clustering–based individual selection strategy is proposed to balance diversity and feasibility. Furthermore, a novel fitness function that integrates UPF, CPF, and diversity information adaptively is designed to balance convergence, feasibility, and diversity in the feasible stage. The superiority of the proposed method is substantiated throught extensive comparison experiments across 34 benchmark functions and parameter analysis experiments. Additionally, application experiments on 16 real–world benchmark CMOPs and an energy consumption optimization problem in copper electrolysis process are conducted, to validate the practical applicability of AIFDEA in diverse real–world and complex industrial environments. Moreover, this paper demonstrates that fusing UPF, CPF, and diversity information adaptively is promising for CMOPs.