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.

最近推出的人工蜂鸟算法 (AHA) 在开发优化问题方面表现出具有竞争力的性能。然而，AHA 的探索能力和利用能力之间存在不平衡，常常过早收敛，精度较低。因此，本文提出一种结合正弦混沌映射策略、Lévy飞行、交叉和更新觅食策略的多策略Boosted混合人工蜂鸟算法LCAHA。首先采用正弦混沌映射策略对LCAHA进行初始化，以获得遍历性较好的种群。其次，引入Lévy飞行可以增强种群的多样性，控制过早收敛，促进种群的稳定性。然后，引入了两种新策略：交叉觅食和更新觅食。新觅食策略的引入进一步增强了勘探开发能力。这三种策略共同提高 AHA 的整体绩效。最后，在 23 个经典测试套件、CEC2017、CEC2019 和 CEC2020 测试套件以及 6 个工程优化案例上检查了 LCAHA 的性能。数值实验结果表明，LCAHA 在解决具有挑战性的优化问题方面提供了非常有前景的数值结果。该算法应用于两个航天器轨迹优化案例。实验结果证明了 LCAHA 在解决实际应用中的适用性和潜力。