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Harris hawks optimization: Algorithm and applications
Open accessAbstract
Abstract In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO). The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce. In this intelligent strategy, several hawks cooperatively pounce a prey from different directions in an attempt to surprise it. Harris hawks can reveal a variety of chasing patterns based on the dynamic nature of scenarios and escaping patterns of the prey. This work mathematically mimics such dynamic patterns and behaviors to develop an optimization algorithm. The effectiveness of the proposed HHO optimizer is checked, through a comparison with other nature-inspired techniques, on 29 benchmark problems and several real-world engineering problems. The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques. Source codes of HHO are publicly available at http://www.alimirjalili.com/HHO.html and http://www.evo-ml.com/2019/03/02/hho .
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Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Holland, J. H. (1992) · Scientific American · doi:10.1038/scientificamerican0792-66 · Cited by 5,764
Feeding innovations and forebrain size in birds
LEFEBVRE, L., WHITTLE, P., LASCARIS, E., & FINKELSTEIN, A. (1997) · Animal Behaviour · doi:10.1006/anbe.1996.0330 · Cited by 579
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Jia, H., Rao, H., Wen, C., & Mirjalili, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10567-4 · Cited by 644
Self-citationHeap-based optimizer inspired by corporate rank hierarchy for global optimization
Askari, Q., Saeed, M., & Younas, I. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113702 · Cited by 380
A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem
Sharma, M., & Kaur, P. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09412-6 · Cited by 303
Metadata
- Authors
- Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 97
- Pages
- 849-872
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2919979744
- Cited by
- 6,324
- References
- 5
- FWCI
- 260.05
- Type
- journal article
- Language
- en
- Open access
- green
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