Harris hawks optimization (HHO)
Source codes for the paper: Harris hawks optimization: Algorithm and applications https://www.sciencedirect.com/science/article/pii/S0167739X18313530
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 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 and related supplementary materials are publicly available at
https://aliasgharheidari.com/HHO.html
http://www.evo-ml.com/2019/03/02/hho.
https://www.researchgate.net/project/Harris-hawks-optimization-HHO-Algorithm-and-applications
https://www.researchgate.net/profile/Ali_Asghar_Heidari
You can run the HHO code online without any installed MATLAB software
https://doi.org/10.24433/CO.1455672.v1
Main paper:
Harris hawks optimization: Algorithm and applications Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, Huiling Chen Future Generation Computer Systems, DOI: https://doi.org/10.1016/j.future.2019.02.028
Author, inventor and programmer: Ali Asghar Heidari
PhD research intern, Department of Computer Science, School of Computing, National University of Singapore, Singapore Exceptionally Talented Ph. DC funded by Iran's National Elites Foundation (INEF), University of Tehran
e-Mail: [email protected], [email protected]
[email protected], [email protected]
Homepage: https://www.researchgate.net/profile/Ali_Asghar_Heidari
Co-authors: Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Hui-Ling Chen
Homepage: http://www.evo-ml.com/2019/03/02/hho/
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