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Wolf Pack Algorithm for Unconstrained Global Optimization

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  • Hu-Sheng Wu
  • Feng-Ming Zhang

Abstract

The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.

Suggested Citation

  • Hu-Sheng Wu & Feng-Ming Zhang, 2014. "Wolf Pack Algorithm for Unconstrained Global Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-17, March.
    • Handle: RePEc:hin:jnlmpe:465082
    DOI: 10.1155/2014/465082
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    Cited by:

    1. Mohamed Abdel-Basset & Reda Mohamed & Safaa Saber & S. S. Askar & Mohamed Abouhawwash, 2021. "Modified Flower Pollination Algorithm for Global Optimization," Mathematics, MDPI, vol. 9(14), pages 1-37, July.
    2. Kottath, Rahul & Singh, Priyanka, 2023. "Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem," Energy, Elsevier, vol. 263(PC).
    3. Shuxin Wang & Hairong You & Yinggao Yue & Li Cao, 2021. "A novel topology optimization of coverage-oriented strategy for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 17(4), pages 15501477219, April.
    4. A. S. Syed Shahul Hameed & Narendran Rajagopalan, 2022. "SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization," Mathematics, MDPI, vol. 10(5), pages 1-24, March.

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