Adaptive Search Range and Multi-Mutation Strategies for Differential Evolution

In this paper, an improved DE is proposed to improve optimization performance by involving four searching strategies: current-to-better mutation, real-random-mutation, sharing mutation, and focused search. When evolution speed is standstill, sharing mutation can increase the search depth; in addition, real-random mutation can disturb individuals and can help individuals diverge to local optimum, focused search can do large-scale searches around the best particle. When the evolution progresses well, current-to-better mutation will drive individuals to the correct evolution direction. Experiments were conducted on all of CEC 2005 test functions, include unimodal, multimodal and hybrid composition functions, to present performance of the proposed method and to compare with 5 variants of DE includes JADE, jDE, SaDE, DEGL and MDE_pBX. The proposed method exhibits better performance than other five related works in solving most the test functions.