Gravitational search algorithm with Gaussian mutation strategy
by Zhaolu Guo; Huogen Yang; Songhua Liu; Xiaosheng Liu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 12, No. 2, 2017

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.

Online publication date: Tue, 16-May-2017

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