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Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

Author

Listed:
  • Omer Ali

    (Department of CS, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Qamar Abbas

    (Department of CS, International Islamic University Islamabad, Islamabad 44000, Pakistan)

  • Khalid Mahmood

    (Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan 29220, Pakistan)

  • Ernesto Bautista Thompson

    (Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Fundación Universitaria Internacional de Colombia, Bogotá, Colombia)

  • Jon Arambarri

    (Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
    Universidade Internacional do Cuanza, Cuito, Bié, Angola)

  • Imran Ashraf

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea)

Abstract

Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment without parameters like the inertia weight w . The PPSO algorithm performs well for small-sized populations but needs improvements for large populations in the case of rapidly growing complex problems and dimensions. This study introduces a competitive coevolution process to enhance the capability of PPSO for global optimization problems. Competitive coevolution disintegrates the problem into multiple sub-problems, and these sub-swarms coevolve for a better solution. The best solution is selected and replaced with the current sub-swarm for the next competition. This process increases population diversity, reduces premature convergence, and increases the memory efficiency of PPSO. Simulation results using PPSO, fuzzy-dominance-based many-objective particle swarm optimization (FMPSO), and improved competitive multi-swarm PPSO (ICPPSO) are generated to assess the convergence power of the proposed algorithm. The experimental results show that ICPPSO achieves a dominating performance. The ICPPSO results for the average fitness show average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO. The Wilcoxon statistical significance test also confirms a significant difference in the performance of the ICPPSO, PPSO, and FMPSO algorithms at a 0.05 significance level.

Suggested Citation

  • Omer Ali & Qamar Abbas & Khalid Mahmood & Ernesto Bautista Thompson & Jon Arambarri & Imran Ashraf, 2023. "Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems," Mathematics, MDPI, vol. 11(21), pages 1-28, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4406-:d:1266120
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    References listed on IDEAS

    as
    1. Lining Xing & Jun Li & Zhaoquan Cai & Feng Hou, 2023. "Evolutionary Optimization of Energy Consumption and Makespan of Workflow Execution in Clouds," Mathematics, MDPI, vol. 11(9), pages 1-18, April.
    2. Mengnan Tian & Yanghan Gao & Xingshi He & Qingqing Zhang & Yanhui Meng, 2023. "Differential Evolution with Group-Based Competitive Control Parameter Setting for Numerical Optimization," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
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