MSI-HHO: Multi-Strategy Improved HHO Algorithm for Global Optimization

Haosen Wang, Jun Tang () and Qingtao Pan
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Haosen Wang: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Jun Tang: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Qingtao Pan: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 3, 1-24

Abstract: The Harris Hawks Optimization algorithm (HHO) is a sophisticated metaheuristic technique that draws inspiration from the hunting process of Harris hawks, which has gained attention in recent years. However, despite its promising features, the algorithm exhibits certain limitations, including the tendency to converge to local optima and a relatively slow convergence speed. In this paper, we propose the multi-strategy improved HHO algorithm (MSI-HHO) as an enhancement to the standard HHO algorithm, which adopts three strategies to improve its performance, namely, inverted S-shaped escape energy, a stochastic learning mechanism based on Gaussian mutation, and refracted opposition-based learning. At the same time, we conduct a comprehensive comparison between our proposed MSI-HHO algorithm with the standard HHO algorithm and five other well-known metaheuristic optimization algorithms. Extensive simulation experiments are conducted on both the 23 classical benchmark functions and the IEEE CEC 2020 benchmark functions. Then, the results of the non-parametric tests indicate that the MSI-HHO algorithm outperforms six other comparative algorithms at a significance level of 0.05 or greater. Additionally, the visualization analysis demonstrates the superior convergence speed and accuracy of the MSI-HHO algorithm, providing evidence of its robust performance.

Keywords: optimization; global optimization; metaheuristic algorithm; Harris hawks optimization; MSI-HHO (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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