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Self balanced particle swarm optimization

Author

Listed:
  • Pawan Bhambu

    (Arya College of Engineering & IT)

  • Sandeep Kumar

    (Jagannath University)

  • Kavita Sharma

    (Government Polytechnic College)

Abstract

In the field of swarm intelligence inspired algorithms, particle swarm optimization (PSO) is a renowned meta-heuristic due to its simplicity, performance, and implementation. However, the PSO also have some downsides like stagnation and slow convergence due to improper balance between the diversification and convergence abilities of the population. Therefore, in this paper, solution search process of PSO algorithm is modified to balance the organization of the individuals in the search space. In the proposed approach, artificial bee colony (ABC) algorithm inspired fitness-based solution search process is incorporated with the PSO algorithm. The proposed approach is tested over 20 unbiased benchmark functions, and the reported results are compared with PSO 2011, ABC, differential evaluation, self-adaptive acceleration factor in PSO, and Mean PSO algorithms through proper statistical analyses.

Suggested Citation

  • Pawan Bhambu & Sandeep Kumar & Kavita Sharma, 2018. "Self balanced particle swarm optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 774-783, August.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:4:d:10.1007_s13198-017-0642-4
    DOI: 10.1007/s13198-017-0642-4
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