Improved Aquila Optimizer Algorithm with Multi-Strategy Fusion

While the Aquila Optimizer (AO) demonstrates commendable performance in global exploration and search efficiency, it is constrained by tendencies toward premature convergence and limited accuracy during local exploitation phases. To mitigate these shortcomings, our research proposes an enhanced variant of the algorithm, designated as the Improved Aquila Optimizer (IAO). First, a chaotic Tent sequence is employed to initialize the population, enhancing diversity. Second, refraction opposition-based learning is incorporated to avoid premature convergence. Finally, integrate it with the African Vultures Optimization Algorithm (AVOA) to improve the performance of the AO algorithm in its exploitation stage. Through the comparison of optimization simulations on unimodal and multimodal test functions with four other optimization algorithms, the results show that the convergence speed and optimization accuracy of the IAO algorithm have been significantly improved.