Improved Chaotic Aquila Optimization Algorithm Based on Artificial Hummingbird Algorithm

The widespread presence of organisms in nature and their unique ways of obtaining nourishment has piqued the curiosity of scientists, leading them to create mathematical models that mimic these organisms. These models have found applications in resolving math problems that are typically challenging and require substantial effort to solve. However, these models may have limitations and may require modifications to enhance their performance. In this study, two methods were employed to ache the optimal solution. The initial phase involved utilizing The Aquila Optimization Algorithm (AO) in combination with the chaos function and chaotic tent function to obtain the optimal solution. This phase served as the foundation of the work. Subsequently, the first phase was further refined by incorporating the artificial hummingbird algorithm (AHA) into the model. This hybridization process entailed integrating communities and equations, and the optimal solution was obtained by performing 1000 iterations in both phases. The research was carried out using MATLAB 2021 as the software platform, providing a robust framework for implementing and evaluating the proposed methods.