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A salp swarm algorithm based on Harris Eagle foraging strategy

Author

Listed:
  • Zhang, Xuncai
  • Wang, Shida
  • Zhao, Kai
  • Wang, Yanfeng

Abstract

For the operation of the salp swarm algorithm(SSA), the leaders fall into local optimum, which leads to the population falling into local optimum problem. In this paper, a salp swarm algorithm based on Harris Eagle foraging strategy is proposed(ISSAHF). Harris hawk optimization algorithms of four types of feeding mechanism are integrated into salp location update process optimization algorithm follower. To strengthen the exploitation of potential areas, complemented by a multi-point leadership crossover strategy to maintain a balance between exploitation and exploration. The performance of the proposed ISSAHF was compared, and Wilcoxon’s statistical analysis was performed on 20 benchmark functions including unimodal, multimodal, and partial CEC2014. Finally, ISSAHF is applied to three practical engineering optimization problems to evaluate its optimization performance further.

Suggested Citation

  • Zhang, Xuncai & Wang, Shida & Zhao, Kai & Wang, Yanfeng, 2023. "A salp swarm algorithm based on Harris Eagle foraging strategy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 858-877.
    • Handle: RePEc:eee:matcom:v:203:y:2023:i:c:p:858-877
    DOI: 10.1016/j.matcom.2022.07.018
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    References listed on IDEAS

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    1. Moustafa, Mahmoud & Mohd, Mohd Hafiz & Ismail, Ahmad Izani & Abdullah, Farah Aini, 2018. "Dynamical analysis of a fractional-order Rosenzweig–MacArthur model incorporating a prey refuge," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 1-13.
    2. Yuqi Fan & Junpeng Shao & Guitao Sun & Xuan Shao, 2020. "A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems," Complexity, Hindawi, vol. 2020, pages 1-17, November.
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    Cited by:

    1. Shahad Ibrahim Mohammed & Nazar K. Hussein & Outman Haddani & Mansourah Aljohani & Mohammed Abdulrazaq Alkahya & Mohammed Qaraad, 2024. "Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization," Mathematics, MDPI, vol. 12(2), pages 1-39, January.
    2. Zhang, Hongbo & Qin, Xiwen & Gao, Xueliang & Zhang, Siqi & Tian, Yunsheng & Zhang, Wei, 2024. "Improved salp swarm algorithm based on Newton interpolation and cosine opposition-based learning for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 544-558.

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