Abstract: In order to improve the convergence speed and solution accuracy of the basic artificial hummingbird algorithm, a multi-strategy fusion artificial hummingbird algorithm is proposed. Firstly, the hummingbird position is initialized by the chaotic reverse communication strategy to improve the diversity of the initial population. Secondly, to coordinate global exploration and local search, the probability dynamic adjustment function is designed to control the guided foraging and regional foraging behaviors of hummingbirds, and the adaptive spiral is introduced to improve the migration foraging behavior. Finally, the location of the optimal hummingbird is disturbed by the Cauchy Gaussian mutation strategy to improve the algorithm's ability to jump out of the local optimum. Finally, 9 benchmark functions are chosen to evaluate the proposed algorithm in simulation experiments, which are compared with the other five latest optimization algorithms. Simulation results show that the proposed algorithm has a faster convergence speed, higher accuracy, and stronger stability. 

Key words: artificial hummingbird algorithm, initialization population, adaptive adjustment, spiral search, disturbance mutation