摘要
Differential evolution （DE） is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.
Differential evolution （DE） is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.
基金
Acknowledgements The authors would like to thank the anonymous reviewers for their very constructive and helpful suggestions. This work was supported in part by the National Basic Research Program （973 Program） of China （2011CB013104）, in part by the Innovation-driven Plan in Central South University （2015CXS012 and 2015CX007）, in part by the National Natural Science Foundation of China （Grant Nos. 61273314 and 61673397）, in part by the EU Horizon 2020 Marie Sldodowska-Curie Individual Fellowships （Project ID： 661327）, in part by the Hunan Provincial Natural Science Fund for Distinguished Young Scholars （2016JJ1018）, in part by the Program for New Century Excellent Talents in University （NCET-13-0596）, and in part by State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology.
