Title: Accelerating Gaussian bare-bones differential evolution using neighbourhood mutation
Authors: Liang Chen; Wenjun Wang; Hui Wang
Addresses: School of Computer Science, China University of Geosciences, Wuhan 430074, China ' School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Abstract: Gaussian bare-bones differential evolution (GBDE) is a new DE algorithm which employs Gaussian random sampling to generate mutant vectors. Though this method can maintain population diversity and enhance the global search ability, it may result in slow convergence rate. In this paper, we present an improved GBDE (IGBDE) algorithm by using neighbourhood mutation to accelerate the evolution. Moreover, a modified parameter control method is utilised to adjust the crossover rate (CR). To verify the performance of our approach, 13 well-known benchmark functions are tested in the experiments. Simulation results show that IGBDE outperforms the original GBDE in terms of solution accuracy and convergence speed.
Keywords: Gaussian bare-bones differential evolution; GBDE; neighbourhood mutation; adaptive parameter control; adaptive control; numerical optimisation; random sampling; mutant vectors; crossover rate; simulation.
International Journal of Computing Science and Mathematics, 2013 Vol.4 No.3, pp.266 - 276
Received: 16 May 2013
Accepted: 20 Jun 2013
Published online: 10 May 2014 *