Title: An enhanced gravitational search algorithm for global optimisation

Authors: Zhaolu Guo; Haixia Huang; Huogen Yang; Shenwen Wang; Hui Wang

Addresses: Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, Ganzhou 341000, China ' School of Literature and Law, 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 Engineering, Shijiazhuang University of Economics, Shijiazhuang 050031, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Abstract: Numerous problems in science and engineering can be converted into optimisation problems. Gravitational Search Algorithm (GSA) is a newly developed optimisation algorithm inspired by Newton's law of gravity and law of motion. However, the traditional GSA tends to suffer from trapping in local minima when solving complex problems. This paper proposes an enhanced gravitational search algorithm (GOGSA), which utilises the generalised opposition-based learning to enhance the search ability. Experiments are conducted on 13 classical test functions. The experimental results and analysis demonstrate that GOGSA can obtain better performance on the majority of the test functions.

Keywords: evolutionary algorithms; global optimisation; gravitational search algorithm; GSA; opposition-based learning; OBL; search ability.

DOI: 10.1504/IJWMC.2015.073102

International Journal of Wireless and Mobile Computing, 2015 Vol.9 No.3, pp.273 - 280

Received: 11 May 2015
Accepted: 02 Jun 2015
Published online: 19 Nov 2015 *

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