A hybrid particle swarm optimization algorithm for high-dimensional problems

  • Authors:

  • DongLi Jia;GuoXin Zheng;BoYang Qu;Muhammad Khurram Khan

  • Affiliations:

  • School of Information and Electronic Engineering, Hebei University of Engineering, Handan 056038, China and Key Laboratory of Special Fiber Optics and Optical Access Networks, Shanghai University, ...;School of Information and Electronic Engineering, Hebei University of Engineering, Handan 056038, China;School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore;Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11653, Saudi Arabia

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2011

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Abstract

In recent years, particle swarm optimization (PSO) emerges as a new optimization scheme that has attracted substantial research interest due to its simplicity and efficiency. However, when applied to high-dimensional problems, PSO suffers from premature convergence problem which results in a low optimization precision or even failure. To remedy this fault, this paper proposes a novel memetic PSO (CGPSO) algorithm which combines the canonical PSO with a Chaotic and Gaussian local search procedure. In the initial evolution phase, CGPSO explores a wide search space that helps avoid premature convergence through Chaotic local search. Then in the following run phase, CGPSO refines the solutions through Gaussian optimization. To evaluate the effectiveness and efficiency of the CGPSO algorithm, thirteen high dimensional non-linear scalable benchmark functions were examined. Results show that, compared to the standard PSO, CGPSO is more effective, faster to converge, and less sensitive to the function dimensions. The CGPSO was also compared with two PSO variants, CPSO-H, DMS-L-PSO, and two memetic optimizers, DEachSPX and MA-S2. CGPSO is able to generate a better, or at least comparable, performance in terms of optimization accuracy. So it can be safely concluded that the proposed CGPSO is an efficient optimization scheme for solving high-dimensional problems.