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Harris hawks optimization: a comprehensive review of recent variants and applications

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Abstract

Harris hawks optimizer (HHO) has received widespread attention among researchers in terms of the performance, quality of results, and its acceptable convergence in dealing with different applications in real-world problems. This increased interest led to the emergence of many versions of HHO applied to various optimization problems in different fields. Therefore, this study aims to identify, retrieve, summarize, and analyze the critical studies related to the development of HHO. For this aim, we applied a review methodology. The applied methodology led to identified and selection of 69 related studies from different electronic sources. The review result revealed that although HHO algorithm is still in the infant stage, its superiority over several well-established metaheuristic algorithms in terms of speed and accuracy for addressing various benchmark problems and tackling several real-world optimization problems has been clearly observed. The HHO algorithm was evaluated, and its strengths and weaknesses were discussed. This review not only suggested possible future directions in this domain but also serves as a comprehensive source of information about HHO and HHO variants for future researchers due to the inclusion of charts and tabular comparison across a wide variety of attributes. A public website supports open access to this research and also source codes of the HHO in a different language and its supplementary materials at https://aliasgharheidari.com/HHO.html.

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taken from the initial paper Heidari et al. [1]

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Notes

  1. The access to materials of this research is available at: https://aliasgharheidari.com/publications/HHOSSA.html

  2. The access to materials of this research is available at: https://aliasgharheidari.com/publications/MCETHHO.html

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Acknowledgements

We acknowledge the comments of the anonymous reviewers that highly enhanced the quality of this research.

Author information

Authors and Affiliations

  1. College of Computing and Informatics, Saudi Electronic University, Abha, Saudi Arabia

    Hamzeh Mohammad Alabool & Deemah Alarabiat

  2. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

  3. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

  4. Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore

    Ali Asghar Heidari

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  1. Hamzeh Mohammad Alabool
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  3. Laith Abualigah
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Appendix: selected studies

Appendix: selected studies

[S1]

Fan, Q., Chen, Z. and Xia, Z., 2020. A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems. Soft Computing, pp.1–19

[S2]

Kamboj, V.K., Nandi, A., Bhadoria, A. and Sehgal, S., 2020. An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Applied Soft Computing, 89, p.106018

[S3]

Dhawale, D. and Kamboj, V.K., 2020, January. hHHO-IGWO: A New Hybrid Harris Hawks Optimizer for Solving Global Optimization Problems. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 52–57). IEEE

[S4]

Chen, H., Heidari, A.A., Chen, H., Wang, M., Pan, Z. and Gandomi, A.H., 2020. Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. Future Generation Computer Systems

[S5]

Gupta, S., Deep, K., Heidari, A.A., Moayedi, H. and Wang, M., 2020. Opposition-based Learning Harris Hawks Optimization with Advanced Transition Rules: Principles and Analysis. Expert Systems with Applications, p.113510

[S6]

Zheng-Ming, G.A.O., Juan, Z.H.A.O., Yu-Rong, H.U. and Chen, H.F., 2019, October. The improved Harris hawk optimization algorithm with the Tent map. In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE) (pp. 336–339). IEEE

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Qu, C., He, W., Peng, X. and Peng, X., 2020. Harris Hawks Optimization with Information Exchange. Applied Mathematical Modelling

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[S11]

Wei, Y., Lv, H., Chen, M., Wang, M., Heidari, A.A., Chen, H. and Li, C., 2020. Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer. IEEE Access, 8, pp.76841–76,855

[S12]

Menesy, A.S., Sultan, H.M., Selim, A., Ashmawy, M.G. and Kamel, S., 2019. Developing and Applying Chaotic Harris Hawks Optimization Technique for Extracting Parameters of Several Proton Exchange Membrane Fuel Cell Stacks. IEEE Access

[S13]

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Yousri, D., Babu, T.S. and Fathy, A., 2020. Recent methodology based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants. Sustainable Energy, Grids and Networks, p.100352

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Jia, H., Lang, C., Oliva, D., Song, W. and Peng, X., 2019. Dynamic Harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sensing, 11(12), p.1421

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Alabool, H.M., Alarabiat, D., Abualigah, L. et al. Harris hawks optimization: a comprehensive review of recent variants and applications. Neural Comput & Applic 33, 8939–8980 (2021). https://doi.org/10.1007/s00521-021-05720-5

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