A New Repair Operator for Multi-objective Evolutionary Algorithm in Constrained Optimization Problems
This work addresses incremental improvements in evolutionary algorithms for researchers in optimization, focusing on domain-specific constrained problems.
The authors tackled constrained multi-objective optimization problems by proposing a new repair operator that uses a reversed correction strategy to fix constraint violations and avoid local optima, integrating it into MOEA/D and NSGA-II, and showing it outperforms other operators on benchmark problems in convergence and diversity.
In this paper, we design a set of multi-objective constrained optimization problems (MCOPs) and propose a new repair operator to address them. The proposed repair operator is used to fix the solutions that violate the box constraints. More specifically, it employs a reversed correction strategy that can effectively avoid the population falling into local optimum. In addition, we integrate the proposed repair operator into two classical multi-objective evolutionary algorithms MOEA/D and NSGA-II. The proposed repair operator is compared with other two kinds of commonly used repair operators on benchmark problems CTPs and MCOPs. The experiment results demonstrate that our proposed approach is very effective in terms of convergence and diversity.