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A novel improved accelerated particle swarm optimization algorithm for global numerical optimization

Gai-Ge Wang (School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, China)
Amir Hossein Gandomi (Department of Civil Engineering, The University of Akron, Akron, Ohio, USA)
Xin-She Yang (School of Engineering and Information Sciences, Middlesex University, London, UK)
Amir Hossein Alavi (Department of Civil and Environmental Engineering, Michigan State University, East Lansing, Michigan, USA)

Engineering Computations

ISSN: 0264-4401

Article publication date: 30 September 2014




Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems.


The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence.


A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO.


A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.



Wang, G.-G., Hossein Gandomi, A., Yang, X.-S. and Hossein Alavi, A. (2014), "A novel improved accelerated particle swarm optimization algorithm for global numerical optimization", Engineering Computations, Vol. 31 No. 7, pp. 1198-1220.



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