Real Random Mutation Strategy for Differential Evolution

Abstract

In this paper, an improved DE is proposed to improve optimization performance by implementing three new schemes: sharing mutation, current-to-better mutation and real-random-mutation. 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. When the evolution progresses well, current-to-better mutation will drive individuals to the correct evolution direction. Experiments were conducted on 15 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 all the test functions.