An archive-based two-stage evolutionary algorithm for constrained multi-objective optimization problems

An important factor in constrained multi-objective evolutionary algorithms (CMOEAs) is how to make optimal use of the information of feasible and infeasible solutions. To fully utilize these promising solutions, this paper proposes an archive-based two-stage evolutionary algorithm, called AT-CMOEA, for solving constrained multi-objective optimization problems (CMOPs). AT-CMOEA divides the evolutionary process into two stages - exploration and exploitation. The purpose of the exploration stage is to encourage a broader exploration of the search space to discover some promising regions, which is achieved by two populations with different priorities of constraints and objectives. In the exploitation stage, the goal is to obtain a set of well-distributed Pareto-optimal solutions by utilizing the useful archived information found during the exploration stage, allowing the search resources to be focused on the region of interest. Then, the two populations cooperatively converge to the constrained Pareto front (CPF). Comprehensive experiments on a series of benchmark test problems and three real-world CMOPs demonstrate the competitiveness of our method when compared with other representative algorithms in terms of effectiveness and reliability in finding a set of well-distributed optimal solutions.

Automated summary

This paper proposes an archive-based two-stage evolutionary algorithm (AT-CMOEA) for solving constrained multi-objective optimization problems (CMOPs), which aims to focus search resources on the region of interest through the exploration and exploitation stages. The method shows competitiveness in effectiveness and reliability in finding a set of well-distributed optimal solutions compared to other representative algorithms.