article · Electronics
This research reviews the Harris Hawk Optimizer (HHO), a recent population-based metaheuristic algorithm inspired by hawk hunting behaviour. HHO uses a unique exploration and exploitation strategy across multiple search phases for optimisation. The paper examines HHO's applications and developments, highlighting its popularity among swarm-based techniques. Experiments compared HHO against nine other state-of-the-art algorithms using the CEC2005 and CEC2017 benchmark functions, demonstrating HHO's power and effectiveness. The review also offers insights into potential future research directions, including new HHO variants and broader applications.
Optimisation algorithms are vital for solving complex problems across many domains. This research highlights the effectiveness of the Harris Hawk Optimizer, a relatively new algorithm, by comparing it with established methods. This understanding can guide researchers in selecting efficient optimisation tools and inspire further development in the field.
This research focuses on evaluating and reviewing an optimisation algorithm, the Harris Hawk Optimizer. While optimisation algorithms have broad applicability in fields like engineering design, logistics, and machine learning, the abstract does not indicate specific application pathways or a readiness level for commercial use. It suggests potential for future widespread applications.
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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
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DOI: 10.3390/electronics11121919
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