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A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

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  • Weitian Lin
  • Zhigang Lian
  • Xingsheng Gu
  • Bin Jiao

Abstract

Particle swarm optimization algorithm (PSOA) is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA), and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA). Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

Suggested Citation

  • Weitian Lin & Zhigang Lian & Xingsheng Gu & Bin Jiao, 2014. "A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, March.
    • Handle: RePEc:hin:jnlmpe:905712
    DOI: 10.1155/2014/905712
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    Cited by:

    1. Srinivasan Vadivel & Boopathi C. Sengodan & Sridhar Ramasamy & Mominul Ahsan & Julfikar Haider & Eduardo M. G. Rodrigues, 2022. "Social Grouping Algorithm Aided Maximum Power Point Tracking Scheme for Partial Shaded Photovoltaic Array," Energies, MDPI, vol. 15(6), pages 1-17, March.

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