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A Study of Global Optimization Using Particle Swarms

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  • Jaco Schutte
  • Albert Groenwold

Abstract

A number of recently proposed variants of the particle swarm optimization algorithm (PSOA) are applied to an extended Dixon-Szeg und constrained test set in global optimization. Of the variants considered, it is shown that constriction as proposed by Clerc, and dynamic inertia and maximum velocity reduction as proposed by Fourie and Groenwold, represent the main contenders from a cost efficiency point of view. A parameter sensitivity analysis is then performed for these two variants in the interests of finding a reliable general purpose off-the-shelf PSOA for global optimization. In doing so, it is shown that inclusion of dynamic inertia renders the PSOA relatively insensitive to the values of the cognitive and social scaling factors. Copyright Springer Science+Business Media New York 2005

Suggested Citation

  • Jaco Schutte & Albert Groenwold, 2005. "A Study of Global Optimization Using Particle Swarms," Journal of Global Optimization, Springer, vol. 31(1), pages 93-108, January.
    • Handle: RePEc:spr:jglopt:v:31:y:2005:i:1:p:93-108
    DOI: 10.1007/s10898-003-6454-x
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    Cited by:

    1. Babaei, Sadra & Sepehri, Mohammad Mehdi & Babaei, Edris, 2015. "Multi-objective portfolio optimization considering the dependence structure of asset returns," European Journal of Operational Research, Elsevier, vol. 244(2), pages 525-539.
    2. Thomas Weise & Yuezhong Wu & Raymond Chiong & Ke Tang & Jörg Lässig, 2016. "Global versus local search: the impact of population sizes on evolutionary algorithm performance," Journal of Global Optimization, Springer, vol. 66(3), pages 511-534, November.
    3. Gökalp Erbeyoğlu & Ümit Bilge, 2016. "PSO-based and SA-based metaheuristics for bilinear programming problems: an application to the pooling problem," Journal of Heuristics, Springer, vol. 22(2), pages 147-179, April.
    4. Emile Glorieux & Bo Svensson & Fredrik Danielsson & Bengt Lennartson, 2017. "Constructive cooperative coevolution for large-scale global optimisation," Journal of Heuristics, Springer, vol. 23(6), pages 449-469, December.
    5. Bigdeli, Nooshin, 2015. "Optimal management of hybrid PV/fuel cell/battery power system: A comparison of optimal hybrid approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 377-393.
    6. Wan, Chunqiu & Wang, Jun & Yang, Geng & Gu, Huajie & Zhang, Xing, 2012. "Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy," Renewable Energy, Elsevier, vol. 48(C), pages 276-286.
    7. Ahmad Wedyan & Jacqueline Whalley & Ajit Narayanan, 2017. "Hydrological Cycle Algorithm for Continuous Optimization Problems," Journal of Optimization, Hindawi, vol. 2017, pages 1-25, December.

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