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Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

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  • Jing Li
  • Yifei Sun
  • Sicheng Hou
  • Lianbo Ma

Abstract

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.

Suggested Citation

  • Jing Li & Yifei Sun & Sicheng Hou & Lianbo Ma, 2021. "Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-13, June.
    • Handle: RePEc:hin:jnddns:8378579
    DOI: 10.1155/2021/8378579
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    Cited by:

    1. Couto, Luis. D. & Charkhgard, Mohammad & Karaman, Berke & Job, Nathalie & Kinnaert, Michel, 2023. "Lithium-ion battery design optimization based on a dimensionless reduced-order electrochemical model," Energy, Elsevier, vol. 263(PE).
    2. Gerkani Nezhad Moshizi, Zahra & Bazrafshan, Ommolbanin & Ramezani Etedali, Hadi & Esmaeilpour, Yahya & Collins, Brain, 2023. "Application of inclusive multiple model for the prediction of saffron water footprint," Agricultural Water Management, Elsevier, vol. 277(C).
    3. Cui, Huixia & Chen, Xiangyong & Guo, Ming & Jiao, Yang & Cao, Jinde & Qiu, Jianlong, 2023. "A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).

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