An Artificial Hummingbird Optimization Algorithm Incorporating Adaptive Search and Levy Flight

To address the problems of low accuracy of artificial hummingbird algorithm (AHA) and its tendency to fall into local optimum, we propose a new artificial hummingbird optimization algorithm (ALAHA) integrating adaptive search and Levy flight. First, we use kent mapping in the initial stage of the algorithm to make the distribution of hummingbird individuals more uniform; then, we introduce Levy flight as an adaptive weight factor to adjust the search step size in the AHA guided foraging and territorial foraging stages to improve the global search ability of the population; finally, we perform adaptive distance search around hummingbird individuals according to the population convergence to improve the search accuracy of the algorithm. In this paper, 10 classical benchmark test functions are selected to experiment the algorithm and compared with other algorithms to test the performance of the algorithm from different perspectives. The results show that the improvement of ALAHA algorithm has improved in the aspects of merit-seeking ability, stability and robustness. The algorithm before and after the improvement is also experimented with the rolling bearing design, and the results are found to be improved compared with the previous algorithm.