Constraint Consensus assisted Evolutionary Algorithm for large-scale constrained optimization

Large-scale constrained optimization problems present significant challenges due to their large number of variables and many constraints. Improper handling of these constraints can lead to suboptimal or infeasible solutions. Many existing approaches overlook this aspect. In this paper, we integrate a constraint-objective cooperative coevolution framework with a Constraint Consensus method, known as DBmax (Maximum Direction-based Method), into differential evolution. In this framework, a problem is decomposed into a number of smaller subproblems (subcomponents) using the Recursive Differential Grouping technique, where interactive variables are allocated to one subproblem. By assessing the impact of each group on the objective function and constraint violation, the most suitable group is selected for evolution. Subsequently, the DBmax method is applied adaptively to the infeasible solutions within the chosen group for improving their feasibility. The algorithm was evaluated on 12 test problems, with the experimental results consistently demonstrating its effectiveness by outperforming existing state-of-the-art methods, in terms of the solution’s feasibility and quality. • A new evolutionary framework for solving large-scale constrained optimization problems is proposed. • The algorithm effectively hybridizes evolutionary algorithms with the Maximum Direction-based Method, which facilitates faster feasibility attainment and better solution quality for large-scale problems. • The experiments demonstrate the robustness and effectiveness of the proposed approach, outperforming state-of-the-art methods in terms of solution quality and feasibility.