LCAHA: A hybrid artificial hummingbird algorithm with multi-strategy for engineering applications

The recently introduced Artificial Hummingbird Algorithm (AHA) exhibits competitive performance in developing optimization concerns. However, AHA has an imbalance between exploration and utilization abilities, often prematurely converging with low precision. Therefore, in this paper, a multi-strategy boosted hybrid artificial hummingbird algorithm called LCAHA combined with sinusoidal chaotic map strategy, Lévy flight, cross, and update foraging strategy is proposed. Firstly, LCAHA is initialized by the sinusoidal chaotic map strategy to obtain a population with better ergodicity. Secondly, introducing the Lévy flight can boost the diversity of the population, control premature convergence and boost the stability of the population. Then, two new strategies, cross foraging and update foraging, are introduced. The introduction of new foraging strategies further enhances the exploration and developmental capabilities. These three strategies work together to improve the overall performance of the AHA. Finally, the performance of the LCAHA is examined on 23 classical test suites, the CEC2017, CEC2019, and CEC2020 test suites, and six engineering optimization cases. The numerical experimental results show that LCAHA provides very promising numerical results in solving challenging optimization problems. The algorithm is applied to two spacecraft trajectory optimization cases. The experimental results demonstrate the applicability and potential of the LCAHA in solving practical applications.