Journal of Zhejiang University-SCIENCE A

, Volume 7, Issue 12, pp 1989–1994

Identification of strategy parameters for particle swarm optimizer through Taguchi method

  • Khosla Arun 
  • Kumar Shakti 
  • Aggarwal K.K. 

DOI: 10.1631/jzus.2006.A1989

Cite this article as:
Khosla, A., Kumar, S. & Aggarwal, K. J. Zhejiang Univ. - Sci. A (2006) 7: 1989. doi:10.1631/jzus.2006.A1989


Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size, crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functions—Rosenbrock function and Griewank function—to validate the approach.

Key words

Strategy parameters Particle swarm optimization (PSO) Taguchi method ANOVA 

CLC number

N941 TP301.6 

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Khosla Arun 
    • 1
  • Kumar Shakti 
    • 2
  • Aggarwal K.K. 
    • 3
  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyJalandharIndia
  2. 2.Centre for Advanced TechnologyHaryana Engineering CollegeJagadhariIndia
  3. 3.Vice ChancellorGGS Indraprastha UniversityDelhiIndia

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