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Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization

Author

Listed:
  • Tingting Zou

    (Information Science and Technology College, Dalian Maritime University, Dalian 116026, China)

  • Changyu Wang

    (Information Science and Technology College, Dalian Maritime University, Dalian 116026, China)

Abstract

The Harris Hawks optimization (HHO) is a population-based metaheuristic algorithm; however, it has low diversity and premature convergence in certain problems. This paper proposes an adaptive relative reflection HHO (ARHHO), which increases the diversity of standard HHO, alleviates the problem of stagnation of local optimal solutions, and improves the search accuracy of the algorithm. The main features of the algorithm define nonlinear escape energy and adaptive weights and combine adaptive relative reflection with the HHO algorithm. Furthermore, we prove the computational complexity of the ARHHO algorithm. Finally, the performance of our algorithm is evaluated by comparison with other well-known metaheuristic algorithms on 23 benchmark problems. Experimental results show that our algorithms performs better than the compared algorithms on most of the benchmark functions.

Suggested Citation

  • Tingting Zou & Changyu Wang, 2022. "Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization," Mathematics, MDPI, vol. 10(7), pages 1-19, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1145-:d:786012
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    References listed on IDEAS

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    1. Tadeusz Sawik, 2022. "Balancing cybersecurity in a supply chain under direct and indirect cyber risks," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 766-782, January.
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    Cited by:

    1. Sultan Almotairi & Elsayed Badr & Mustafa Abdul Salam & Alshimaa Dawood, 2023. "Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 11(19), pages 1-27, October.

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