Classification guided differential evolution
Shiyan Shen, Wenyin Gong, Zhihua Cai · 2016
Differential evolution (DE) is an efficient and powerful evolutionary algorithm for numerical optimization. In DE, different search strategies are presented. Generally, different strategies are suitable to different problems, whereas it is difficult to select the best one for a problem at hand. In this paper, we propose a multi-strategy based DE framework based on the classification technique. More specifically, a set of candidate trial vectors are generated by using the multiple strategies, and the best one is chosen as the trial vector according to the classification technique. As an illustration, the extreme learning machine (ELM) is selected as the classification method in our framework. Then, four state-of-the-art DE variants (i.e., SaDE, CoDE, EPSDE, and JADE) are integrated into the framework to implement multi-strategy adaptation in DE. The proposed variants are extensively evaluated on a suite of 13 benchmark optimization problems. Experimental results show that our approach is very competitive with state-of-the-art DE variants.