Harris hawks optimization: Algorithm and applications

• Published in: Future generation computer systems Vol. 97; pp. 849 - 872
• Main Authors: Heidari, Ali Asghar, Mirjalili, Seyedali, Faris, Hossam, Aljarah, Ibrahim, Mafarja, Majdi, Chen, Huiling
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2019
• Subjects: Harris hawks optimization algorithm; Metaheuristic; Nature-inspired computing; Optimization; Swarm intelligence; Metaheuristic; Harris hawks optimization algorithm; Swarm intelligence; Nature-inspired computing; Optimization
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2019.02.028

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. •A mathematical model is proposed to simulate the hunting behavior of Harris’ Hawks.•An optimization algorithm is proposed using the mathematical model.•The proposed HHO algorithm is tested on several benchmarks.•The performance of HHO is also examined on several engineering design problems.•The results show the merits of the HHO algorithm as compared to the existing algorithms.