Differential Evolution with a Species-based Repair Strategy for Constrained Optimization

Evolutionary Algorithms (EAs) with gradient-based repair, which utilize the gradient information of the constraints set, have been proved to be effective. It is known that it would be time-consuming if all infeasible individuals are repaired. Therefore, so far the infeasible individuals to be repaired are randomly selected from the population and the strategy of choosing individuals to be repaired has not been studied yet. In this paper, the Species-based Repair Strategy (SRS) is proposed to select representative infeasible individuals instead of the random selection for gradient-based repair. The proposed SRS strategy has been applied to epsilon DEag which repairs the random selected individuals using the gradient-based repair. The new algorithm is named SRS-epsilon DEag. Experimental results show that SRS-epsilon DEag outperforms epsilon DEag in most benchmarks. Meanwhile, the number of repaired individuals is reduced markedly.