Enhancing differential evolution with effective evolutionary local search in memetic framework

Memetic algorithms (MAs) are widely recognized to have better convergence capability than their conventional counterparts. Due to its good robustness and universality, differential evolution (DE) has been frequently used as the global search method in MAs. However, on account of the limited performance of the conventional local search operators, the performance of previous DE-related MAs still needs further improvement. In this paper, we implement more efficient evolutionary algorithms (EAs) as the local search techniques in an adaptive MA framework to form two MA(DE-LS) variants, and investigate their impacts. In order to comprehensively show the effectiveness and efficiency of MA(DE-LS), we experimentally compare it with state-of-the-art EAs, DE-based MAs and other MAs.