Title: Gravitational search algorithm with Gaussian mutation strategy

Authors: Zhaolu Guo; Huogen Yang; Songhua Liu; Xiaosheng Liu

Addresses: 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 ' Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, Ganzhou 341000, China ' School of Architectural and Surveying & Mapping Engineering, JiangXi University of Science and Technology, Ganzhou 341000, China

Abstract: Gravitational search algorithm (GSA) is an emerging evolutionary algorithm (EA), which has exhibited remarkable performance in many applications. However, the traditional gravitational search algorithm tends to yield slow convergence speed when facing some complicated real-life problems. Aiming at this weakness, a new gravitational search algorithm with Gaussian mutation strategy (GMGSA) is presented. At each generation, GMGSA calculates the centre of the current individual and the global best individual, and then combines the obtained centre information into the Gaussian mutation strategy to generate new individuals. In the experiments, GMGSA is evaluated on a set of well-known benchmark problems. The experimental results indicate that GMGSA can demonstrate promising performance.

Keywords: evolutionary algorithms; global optimisation; gravitational search algorithm; Gaussian mutation.

DOI: 10.1504/IJWMC.2017.084184

International Journal of Wireless and Mobile Computing, 2017 Vol.12 No.2, pp.191 - 197

Received: 29 Nov 2016
Accepted: 06 Jan 2017
Published online: 16 May 2017 *

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