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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. [ABSTRACT FROM AUTHOR]

In this article, an Improved Particle Swarm Optimization (IPSO) is proposed for solving global optimization and hyperparameter optimization. This improvement is proposed to reduce the probability of particles falling into local optimum and alleviate premature convergence and the imbalance between the exploitation and exploration of the Particle Swarm Optimization (PSO). The IPSO benefits from a new search policy named group-based update policy. The initial population of IPSO is grouped by the k-means to form a multisubpopulation, which increases the intragroup learning mechanism of particles and effectively enhances the balance between the exploitation and exploration. The performance of IPSO is evaluated on six representative test functions and one engineering problem. In all experiments, IPSO is compared with PSO and one other state-of-the-art metaheuristics. The results are also analyzed qualitatively and quantitatively. The experimental results show that IPSO is very competitive and often better than other algorithms in the experiments. The results of IPSO on the hyperparameter optimization problem demonstrate its efficiency and robustness. [ABSTRACT FROM AUTHOR]