Differential evolution with a two-stage optimization mechanism for numerical optimization
Zhizhong Liu, Yong Wang, Shengxiang Yang, Zixing Cai · 2016
Differential Evolution (DE) is a popular paradigm of evolutionary algorithms, which has been successfully applied to solve different kinds of optimization problems. To design an effective DE, it is necessary to consider different requirements of the exploration and exploitation at different evolutionary stages. Motivated by this consideration, a new DE with a two-stage optimization mechanism, called TSDE, has been proposed in this paper. In TSDE, based on the number of fitness evaluations, the whole evolutionary process is divided into two stages, namely the former stage and the latter stage. TSDE focuses on improving the search ability in the former stage and emphasizes the convergence in the latter stage. Hence, different trial vector generation strategies have been utilized at different stages. TSDE has been tested on 25 benchmark test functions from IEEE CEC2005 and 30 benchmark test functions from IEEE CEC2014. The experimental results suggest that TSDE performs better than four other state-of-the-art DE variants.