Literature Review of Harris Hawk Optimization Algorithm

Abstract

The Harris Hawks Optimizer is a revolutionary population-based, nature-inspired optimization methodology proposed in this paper (HHO). The cooperative behaviour and pursuit manner of Harris' hawks in nature, known as surprise pounce, is the fundamental inspiration for HHO. Several hawks work together to pounce on a victim from different directions in an attempt to catch it off guard. Based on the dynamic nature of events and the prey's escape behaviours, Harris hawks can display a range of pursuit patterns. To design an optimization algorithm, this work mathematically duplicates such dynamic patterns and behaviours. On 29 benchmark problems and numerous real-world engineering challenges, the effectiveness of the proposed HHO optimizer is tested by comparing it to other nature-inspired approaches. The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well established metaheuristic techniques. When compared to well-established metaheuristic techniques, the statistical results and comparisons reveal that the HHO algorithm delivers highly promising and occasionally competitive outcomes. Keywords: Swarm intelligence, Optimization, Metaheuristic, Harris hawks optimization method, Nature-inspired computing.