What is it about?
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.
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Why is it important?
The article on "An Improved Harris Hawk Optimization Algorithm" is significant because it addresses key limitations of the original HHO algorithm, such as its tendency to get trapped in local optima and its slow convergence speed. By introducing enhancements like Gaussian chaotic mapping and optimized energy parameters, the improved algorithm (GHHO) demonstrates superior performance in solving complex optimization problems. This research is important for advancing optimization techniques, which have applications in various fields such as engineering, data analysis, and artificial intelligence. For more details, you can view the article [here](https://journals.viserdata.com/index.php/mes/article/view/13224).
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This page is a summary of: An Improved Harris Hawk Optimization Algorithm, Mechanical Engineering Science, July 2024, Viser Technology Pte Ltd,
DOI: 10.33142/mes.v6i1.13224.
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