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QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization

Author

Listed:
  • Arnaud Flori

    (Univ Paris Est Creteil, LISSI)

  • Hamouche Oulhadj

    (Univ Paris Est Creteil, LISSI)

  • Patrick Siarry

    (Univ Paris Est Creteil, LISSI)

Abstract

Particle Swarm Optimization (PSO) is a population-based metaheuristic belonging to the class of Swarm Intelligence (SI) algorithms. Nowadays, its effectiveness on many hard problems is no longer to be proven. Nevertheless, it is known to be strongly sensitive on the choice of its settings and weak for local search. In this paper, we propose a new algorithm, called QUAntum Particle Swarm Optimization (QUAPSO) based on quantum superposition to set the velocity PSO parameters, simplifying the settings of the algorithm. Another improvement, inspired by Kangaroo Algorithm (KA), was added to PSO in order to optimize its efficiency in local search. QUAPSO was compared with a set of six well-known algorithms from the literature (two parameter sets of classical PSO, KA, Differential Evolution, Simulated Annealing Particle Swarm Optimization, Bat Algorithm and Simulated Annealing Gaussian Bat Algorithm). The experimental results show that QUAPSO outperforms the competing algorithms on a set of 30 test functions.

Suggested Citation

  • Arnaud Flori & Hamouche Oulhadj & Patrick Siarry, 2022. "QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 525-559, June.
    • Handle: RePEc:spr:coopap:v:82:y:2022:i:2:d:10.1007_s10589-022-00362-2
    DOI: 10.1007/s10589-022-00362-2
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    References listed on IDEAS

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    1. Manoj Dhadwal & Sung Jung & Chang Kim, 2014. "Advanced particle swarm assisted genetic algorithm for constrained optimization problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 781-806, July.
    2. Maurice Clerc, 2010. "Beyond Standard Particle Swarm Optimisation," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(4), pages 46-61, October.
    3. Kalyanmoy Deb & Nikhil Padhye, 2014. "Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms," Computational Optimization and Applications, Springer, vol. 57(3), pages 761-794, April.
    4. Jietao Dong & Linxuan Zhang & Tianyuan Xiao, 2018. "A hybrid PSO/SA algorithm for bi-criteria stochastic line balancing with flexible task times and zoning constraints," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 737-751, April.
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