Authors: Xuemei You; Yinghong Ma; Zhiyuan Liu
Addresses: School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China; Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China ' School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China ' School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China
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.
Keywords: differential evolution; mutation operator; function optimisation; global optimisation; exploitation ability; simulation.
International Journal of Wireless and Mobile Computing, 2016 Vol.10 No.1, pp.56 - 61
Received: 14 May 2015
Accepted: 03 Jun 2015
Published online: 07 Mar 2016 *