Title: Enhanced social emotional optimisation algorithm with elite multi-parent crossover

Authors: Zhaolu Guo; Shenwen Wang; Xuezhi Yue; Baoyong Yin; Changshou Deng; Zhijian Wu

Addresses: Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, Ganzhou 341000, China ' School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China ' Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, Ganzhou 341000, China ' Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, Ganzhou 341000, China ' School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China ' State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China

Abstract: Social emotional optimisation algorithm (SEOA) has been successfully applied in a variety of real-world applications. However, it may suffer from slow convergence rate when solving complex optimisation problems. In order to improve the performance of SEOA on complex optimisation problems, in this paper, an enhanced social emotional optimisation algorithm with elite multi-parent crossover (MCSEOA) is proposed. In MCSEOA, it employs the elite multi-parent crossover operator to exploit the neighbourhood solutions of the current population. The numerical experiments are conducted on 13 classical test functions. Comparison results demonstrate that MCSEOA can significantly improve the performance of the traditional SEOA.

Keywords: evolutionary algorithms; global optimisation; social emotional optimisation; SEOA; multi-parent crossover.

DOI: 10.1504/IJCSM.2016.081694

International Journal of Computing Science and Mathematics, 2016 Vol.7 No.6, pp.568 - 574

Received: 23 May 2016
Accepted: 25 Jun 2016
Published online: 20 Jan 2017 *

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