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Biases in Particle Swarm Optimization

Author

Listed:
  • William M. Spears

    (Swarmotics LLC, USA)

  • Derek T. Green

    (University of Arizona, USA)

  • Diana F. Spears

    (Swarmotics LLC, USA)

Abstract

The most common versions of particle swarm optimization (PSO) algorithms are rotationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the concentration along lines parallel to the coordinate axes. Based on this explicit connection, the authors create fitness functions that are easy or hard for PSO to solve, depending on the rotation of the function.

Suggested Citation

  • William M. Spears & Derek T. Green & Diana F. Spears, 2010. "Biases in Particle Swarm Optimization," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(2), pages 34-57, April.
    • Handle: RePEc:igg:jsir00:v:1:y:2010:i:2:p:34-57
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    Cited by:

    1. Mahamed Omran & Salah al-Sharhan & Ayed Salman & Maurice Clerc, 2013. "Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms," Computational Optimization and Applications, Springer, vol. 56(2), pages 457-480, October.
    2. Dileep, G. & Singh, S.N., 2015. "Maximum power point tracking of solar photovoltaic system using modified perturbation and observation method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 109-129.
    3. Fontes, Dalila B.M.M. & Homayouni, S. Mahdi & Gonçalves, José F., 2023. "A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1140-1157.

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