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The TLTHHO algorithm is proposed to address the shortcomings of Harris Hawks optimization in dealing with certain complex problems that are prone to local optimality, slow convergence, and inability to find the global optimal solution. Firstly, a teaching-learning-based optimization algorithm is used to improve the population's low-quality solutions, enhance communication between populations, and improve solution quality. Secondly, to perturb the global optimal solution and improve the algorithm's ability to jump out of the local optimal solution, a dynamic adaptive t-distribution variation strategy is introduced. Finally, a greedy strategy is used to select between the perturbed individuals and the hawk individuals, thus improving the optimization ability and enhancing the optimization accuracy of the hawk population. The performance of TLTHHO is validated by 10 test functions with unimodal and multimodal characteristics, and the simulation experiments show that the proposed algorithm's performance is effectively improved, and the convergence speed and accuracy are faster and more accurate than the comparison algorithms.
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