An Improved Harris Hawk Optimization Algorithm

Abstract

Aiming at the problems that the original Harris Hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum, this paper proposes an improved Harris Hawk optimization algorithm (GHHO). Firstly, we used a Gaussian chaotic mapping strategy to initialize the positions of individuals in the population, which enriches the initial individual species characteristics. Secondly, by optimizing the energy parameter and introducing the cosine strategy, the algorithm's ability to jump out of the local optimum is enhanced, which improves the performance of the algorithm. Finally, comparison experiments with other intelligent algorithms were conducted on 13 classical test function sets. The results show that GHHO has better performance in all aspects compared to other optimization algorithms. The improved algorithm is more suitable for generalization to real optimization problems.