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Dynamically tuning the population size in particle swarm optimization

Published: 16 March 2008 Publication History

Abstract

In this paper, we investigate the benefits of dynamically varying the population size in the Particle Swarm Optimization (PSO) model. For this purpose, two well-known population resizing techniques, originally developed for Genetic Algorithms (GAs), were adapted to the PSO context, giving birth to the APPSO and PRoFIPSO variants. Contrary to some previous work that has indicated that the PSO model is not sensitive to the population dimension, the simulation results we have obtained over some benchmark numerical optimization problems suggest that the dynamic variation of the number of particles may be instrumental for bringing about performance improvements in long-term runs, mainly when considering high-dimensional problem instances. In general, the novel PSO variants have compared more favorably to their GA counterparts in targeting the optimal solutions. However, regarding PRoFIPSO specifically, the price to be paid in terms of resources used to reach the optimum point is as a rule very high.

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  • (2023)A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization ProblemsACM Transactions on Evolutionary Learning and Optimization10.1145/36048123:4(1-25)Online publication date: 14-Jun-2023
  • (2021)A Population Size Dynamic Reduction Criterion in PSO Algorithms2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504791(1349-1356)Online publication date: 28-Jun-2021
  • (2019)Measuring the curse of population size over swarm intelligence based algorithmsEvolving Systems10.1007/s12530-019-09318-012:3(779-826)Online publication date: 12-Dec-2019
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    cover image ACM Conferences
    SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
    March 2008
    2586 pages
    ISBN:9781595937537
    DOI:10.1145/1363686
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    Published: 16 March 2008

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    Author Tags

    1. numerical optimization
    2. parameter control
    3. population sizing

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    March 16 - 20, 2008
    Fortaleza, Ceara, Brazil

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    View all
    • (2023)A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization ProblemsACM Transactions on Evolutionary Learning and Optimization10.1145/36048123:4(1-25)Online publication date: 14-Jun-2023
    • (2021)A Population Size Dynamic Reduction Criterion in PSO Algorithms2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504791(1349-1356)Online publication date: 28-Jun-2021
    • (2019)Measuring the curse of population size over swarm intelligence based algorithmsEvolving Systems10.1007/s12530-019-09318-012:3(779-826)Online publication date: 12-Dec-2019
    • (2015)State estimation of nonlinear dynamic systems using weighted variance-based adaptive particle swarm optimizationApplied Soft Computing10.1016/j.asoc.2015.04.02934:C(1-17)Online publication date: 1-Sep-2015
    • (2013)A mutation adaptation mechanism for Differential Evolution algorithm2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557553(55-62)Online publication date: Jun-2013
    • (2013)Particle swarm optimization and identification of inelastic material parametersEngineering Computations10.1108/EC-10-2011-011830:7(936-960)Online publication date: 7-Oct-2013
    • (2011)Incremental Social Learning in Particle SwarmsIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics10.1109/TSMCB.2010.205584841:2(368-384)Online publication date: 1-Apr-2011
    • (2008)Incremental Particle Swarm-Guided Local Search for Continuous OptimizationProceedings of the 5th International Workshop on Hybrid Metaheuristics10.1007/978-3-540-88439-2_6(72-86)Online publication date: 8-Oct-2008

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