Abstract: To address the shortcomings of slow convergence speed and susceptibility to local optima in the standard aquila optimization (AO) algorithm, an improved aquila optimization algorithm, TAO algorithm, is proposed by integrating stochastic opposition-based learning and a mutated teaching- learning-based optimization (TLBO) strategy. Firstly, an expanded search strategy is introduced in the initial phase to enhance the diversity of initial space exploration, and differential mutation is employed to improve the quality of optimization. Secondly, a stochastic opposition-based learning strategy is adopted to increase the number of elite individuals, thereby enhancing the algorithm’s search quality. Furthermore, individual positions are updated through t-distribution mutation perturbations to boost the diversity of the search space. Meanwhile, the TLBO strategy is integrated, leveraging the teaching-learning synergy strategy to accelerate the algorithm’s convergence speed. Then, simulation experiments were carried out on 23 functions with diverse characteristics (unimodal, multimodal, and fixed-dimension multimodal) selected from the CEC 2005 benchmark test suite. The results demonstrate that, compared to AO algorithm and several other heuristic intelligent optimization algorithms, TAO algorithm exhibits superior performance in terms of optimization accuracy, convergence, and stability. The Wilcoxon rank-sum test results further verify that the search performance of TAO is significantly different from that of the comparative algorithms, and TAO outperforms the comparison algorithms. Finally, three engineering design optimization cases are introduced to further validate the feasibility of TAO algorithm in solv- ing practical problems.

Key words: aquila optimizer, random opposite-based learning, t-distribution, differential variation, teaching-learning strategy