Adaptive Artificial Hummingbird Algorithm: Enhanced Initialization and Migration Strategies for Continuous Optimization

Due to their complexity and non-linearity, metaheuristic algorithms have become the gold standard in problem solving for those problems that cannot be solved by standard computational solutions. However, the global performance of these algorithms is strongly linked to the population structuring and the mechanism of replacing the worst solutions within the population. In this paper, an Adaptive Artificial Hummingbird Algorithm (AAHA), a new version of the basic AHA, is introduced and designed to enhance performance by studying the impacts of different population initialization methods within a broad and continual migration form. For the initialization phase, four methods: the Gaussian chaotic map, the Sinus chaotic map, the Opposite-based learning (OBL), and the Diagonal Uniform Distribution (DUD) are proposed as an alternative to the random population initialization method. A new strategy is proposed for replacing the worst solution in the migration phase. The new strategy used the best solution as an alternative to the worst solution with simple and effective local search. The proposed strategy stimulates exploitation and exploration when using the best solution and local search, respectively. The proposed AAHA algorithm is tested through various benchmark functions with different characteristics under many statistical indices and tests. Additionally, the AAHA results are benchmarked against other optimization algorithms to assess their effectiveness. The proposed AAHA algorithm outperformed alternatives in both speed and reliability. DUD-based initialization enabled the fastest convergence and optimal solutions. These findings underscore the significance of initialization in metaheuristics and highlight the efficacy of the AAHA algorithm for complex continuous optimization problems.