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Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Author

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  • Narinder Singh
  • S. B. Singh

Abstract

A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants’ strength. Some unimodal, multimodal, and fixed-dimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

Suggested Citation

  • Narinder Singh & S. B. Singh, 2017. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance," Journal of Applied Mathematics, Hindawi, vol. 2017, pages 1-15, November.
    • Handle: RePEc:hin:jnljam:2030489
    DOI: 10.1155/2017/2030489
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    Cited by:

    1. Hazem Hassan Ellithy & Adel M. Taha & Hany M. Hasanien & Mahmoud A. Attia & Adel El-Shahat & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Triple Diode Photovoltaic Models Using Hybrid Particle Swarm and Grey Wolf Optimization," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    2. Saad Dahmani & Djilali Yebdri, 2020. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Reservoir Operation Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4545-4560, December.
    3. Roy, Nibir Baran & Das, Debapriya, 2024. "Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    4. Wenbin Su & Hongbo Wei & Penghua Guo & Ruizhe Guo, 2021. "Remote Monitoring and Fault Diagnosis of Ocean Current Energy Hydraulic Transmission and Control Power Generation System," Energies, MDPI, vol. 14(13), pages 1-18, July.
    5. Dinh-Tu Nguyen & Yuan-Ting Lin & Jeng-Rong Ho & Pi-Cheng Tung & Chih-Kuang Lin, 2023. "Temperature-Controlled Laser Cutting of an Electrical Steel Sheet Using a Novel Fuzzy Logic Controller," Mathematics, MDPI, vol. 11(23), pages 1-20, November.
    6. Manish Kumar Singla & Jyoti Gupta & Beant Singh & Parag Nijhawan & Almoataz Y. Abdelaziz & Adel El-Shahat, 2023. "Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    7. Ajit Kumar Mohanty & Perli Suresh Babu & Surender Reddy Salkuti, 2022. "Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    8. Haijun Liu & Rui Zhou & Xiaoyong Zhong & Yuan Yao & Weifeng Shan & Jing Yuan & Jian Xiao & Yan Ma & Kunpeng Zhang & Zhibin Wang, 2024. "Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO," Mathematics, MDPI, vol. 12(19), pages 1-41, October.

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