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Initial particles position for PSO, in Bound Constrained Optimization


  • E.F. Campana

    () (National Research Council-Maritime Research Centre (CNR-ISEAN))

  • Matteo Diez

    () (National Research Council-Maritime Research Centre (CNR-ISEAN))

  • Giovanni Fasano

    () (Università Ca' Foscari Venice)

  • Daniele Peri

    () (National Research Council-Maritime Research Centre (CNR-ISEAN))


We consider the solution of bound constrained optimization problems, where we assume that the evaluation of the objective function is costly, its derivatives are unavailable and the use of exact derivativefree algorithms may imply a too large computational burden. There is plenty of real applications, e.g. several design optimization problems [1,2], belonging to the latter class, where the objective function must be treated as a Ôblack-boxÕ and automatic differentiation turns to be unsuitable. Since the objective function is often obtained as the result of a simulation, it might be affected also by noise, so that the use of finite differences may be definitely harmful. In this paper we consider the use of the evolutionary Particle Swarm Optimization (PSO) algorithm, where the choice of the parameters is inspired by [4], in order to avoid diverging trajectories of the particles, and help the exploration of the feasible set. Moreover, we extend the ideas in [4] and propose a specific set of initial particles position for the bound constrained problem.

Suggested Citation

  • E.F. Campana & Matteo Diez & Giovanni Fasano & Daniele Peri, 2013. "Initial particles position for PSO, in Bound Constrained Optimization," Working Papers 6, Department of Management, Università Ca' Foscari Venezia.
  • Handle: RePEc:vnm:wpdman:42

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    References listed on IDEAS

    1. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading without Resampling," Working Papers 164, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    2. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, Oxford University Press, vol. 112(2), pages 603-630.
    3. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    4. Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013. "Convergence of outcomes and evolution of strategic behavior in double auctions," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 513-538, July.
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    More about this item


    Bound Constrained Optimization; Discrete Dynamic Linear Systems; Free and Forced Responses; Particles Initial Position.;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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