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Harris hawk2/28/2024 Figure 3 shows all phases of HHO, which are described in the next subsections.Harris' hawks can demonstrate a variety of chasing styles dependent on the dynamic nature of circumstances and escaping patterns of a prey.The hawks occasionally will perform a "leapfrog" motion all over the target site and they rejoin and split several times to actively search for the covered animal, which is usually a rabbit 2.They are known as truly cooperative predators in the raptor realm.In 1997, Louis Lefebvre proposed an approach to measure the avian "IQ" based on the observed innovations in feeding behaviors.Hence, the exploratory behaviors of a well-designed optimizer should have an enriched-enough random nature to efficiently allocate more randomly-generated solutions to different areas of the problem topography during early steps of the searching process.Salcedo-Sanz has deeply reviewed several physic-based optimizers.1 ): Evolutionary Algorithms (EAs), Physics-based, Human-based, and Swarm Intelligence (SI) algorithms. Based on the inspiration, P-metaheuristics can be categorized in four main groups (see Fig.population) are evolved in each iteration of the optimization process. As the name indicates, in the former type, only one solution is processed during the optimization phase, while in the latter type, a set of solutions (i.e.Many real-world problems in machine learning and artificial intelligence have generally a continuous, discrete, constrained or unconstrained nature.Source codes of HHO are publicly available at and. The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques. ![]() 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. This work mathematically mimics such dynamic patterns and behaviors to develop an optimization algorithm. Harris hawks can reveal a variety of chasing patterns based on the dynamic nature of scenarios and escaping patterns of the prey. In this intelligent strategy, several hawks cooperatively pounce a prey from different directions in an attempt to surprise it. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce. Abstract: In this paper, a novel population-based, nature-inspired optimization paradigm is proposed, which is called Harris Hawks Optimizer (HHO).
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