Self balanced particle swarm optimization

  • Pawan Bhambu
  • Sandeep Kumar
  • Kavita Sharma
Original Article


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.


Population based algorithm Swarm intelligence Nature inspired algorithm Optimization 


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Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017

Authors and Affiliations

  1. 1.Arya College of Engineering & ITJaipurIndia
  2. 2.Jagannath UniversityJaipurIndia
  3. 3.Government Polytechnic CollegeKotaIndia

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