Solving Multi-objective Optimization Problem with Complex Constraints Based on Grouping of Constraints

Multi-objective optimization problems with complex constraints are common in practical production. To account for this, we present a complex constrained multi-objective evolutionary optimization algorithm framework based on constraint grouping., which divides the constraints into strong constraints and weak constraints, and then constructs multiple simple optimization sub-problems, each of which corresponds to a different evolutionary population. The subproblem population co-evolves with the original problem population, which helps the original problem to cross the infeasible region and find an effective feasible solution. To verify the effectiveness of the proposed algorithm, the proposed algorithm and four constrained multi-objective evolutionary algorithms are tested and compared on 14 test functions. The results indicate that the proposed algorithm has good performance and strong competitiveness.