Artificial Hummingbird Algorithm Based on Tent Mapping and Levy Mutation

Aiming at the shortcomings of Artificial Hummingbird Algorithm (AHA) such as slower convergence process, poor solution accuracy and early convergence to local extremes, an Artificial Hummingbird Algorithm (TLAHA) based on Tent mapping and Levy mutation is proposed. First, the hummingbird population is initialized with Tent mapping, which ensures the diversity of hummingbirds; second, a nonlinear convergence factor is introduced into the hummingbird’s foraging strategy, which stabilizes the phase of global development and local search of the AHA; and finally, the Levy mutation mechanism is implemented, which mutates the optimal hummingbird through the mutation operator to improve the drawback of the algorithm’s convergence too early. Simulation experiments are conducted in the CEC2021 test set to compare the AHA after adding the improved strategy with the standard AHA and the other six algorithms. Through simulation analysis, the improved AHA search capability is improved, convergence is faster, and the solution is accurate, stability is enhanced, and optimization is better.