A new modified differential evolution for global optimisation Online publication date: Tue, 08-Mar-2016
by Xuemei You; Yinghong Ma; Zhiyuan Liu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 10, No. 1, 2016
Abstract: Differential evolution (DE) is a population-based random optimisation algorithm, which has been used to solve benchmark functions and real-world optimisation problems. The DE has three important operators: mutation, crossover, and selection. The mutation operator in the original DE can hardly balance the exploitation and exploration of the search. In this paper, we design a new mutation operator to improve the exploitation ability of DE. Experiments are carried out on 13 classical test functions. Simulation results show that the new mutation scheme can help DE to find better solutions than three other classical DE mutation strategies.
Online publication date: Tue, 08-Mar-2016
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