Enhancing the search ability of differential evolution through competent leader

摘要

In the field of evolutionary algorithm (EA), differential evolution (DE) is successfully used in various scientific and engineering fields due to its strong global optimisation capability and simple implementation. However, in most of DE, the search is guided by the random or local optimal vectors. That is, DE does not effectively use the good information of population to guide the search. Therefore, to alleviate this drawback and enhance the search ability of DE, a competent leaders guiding strategy (cLGS) is proposed in this paper. The proposed cLGS is inspired by the natural phenomenon that good species usually contain useful information to guide the search of population. With the competent leaders, the good information of population can be utilised effectively during the evolutionary process. By incorporating cLGS into JADE which is a very competitive DE variant, the resulting algorithm, named JADE-cLGS, is proposed. In order to test the effectiveness of JADE-cLGS, a suite of benchmark functions is used. Experimental results demonstrate the high performance of JADE-cLGS by comparing with several DE variants.