Artificial hummingbird algorithm (AHA) is one of the recent bio-inspired meta-heuristic algorithms which is based on hummingbirds’ intelligent behaviours. Just like many meta-heuristic algorithms, it also suffers from freezing in local optima and slow convergence speed. In this paper, the authors have proposed a novel chaotic artificial hummingbird algorithm (ChAHA) obtained by incorporating chaos theory in the original AHA with the aim of escaping it from local minima stagnation along with high convergence rate and more precise results. Firstly, detailed studies have been performed on six different unimodal and multimodal constrained benchmark functions by employing ten different chaotic test mappings in order to determine the most enhanced and efficient one. Later, statistical testing and graphical analysis prove that incorporation of chaotic maps (especially tent map) in AHA improves the original AHA by showing promising performance. Finally, the performance of the ChAHA (with tent map) is also validated by finding the optimum gain values of a fractional order proportional-integral-derivative (FOPID) controller, meticulously tailored to meet the specific requirements of DC motor speed control in MATLAB/Simulink. It has been unambiguously affirmed that the closed loop system with the proposed ChAHA-FOPID controller has better performance than certain pre-existing controllers such as grey wolf optimization based FOPID (GWO-FOPID), atom search optimization based FOPID (ASO-FOPID) and manta ray foraging optimization based FOPID (MRFO-FOPID) controllers. Finally, robustness analysis is also carried out with parameter variations of DC motor and the final simulation results validate the superiority of the proposed approach.

人工蜂鸟算法（AHA）是最近受生物启发的元启发式算法之一，它基于蜂鸟的智能行为。就像许多元启发式算法一样，它也存在局部最优冻结和收敛速度慢的问题。在本文中，作者提出了一种新颖的混沌人工蜂鸟算法（ChAHA），将混沌理论融入原始AHA中，旨在摆脱局部极小值停滞，同时具有高收敛速度和更精确的结果。首先，通过采用十种不同的混沌测试映射对六种不同的单峰和多峰约束基准函数进行了详细研究，以确定最增强和最有效的一个。后来，统计测试和图形分析证明，在 AHA 中加入混沌地图（尤其是帐篷地图）通过显示出有希望的性能来改进原始 AHA。最后，还通过寻找分数阶比例积分微分 (FOPID) 控制器的最佳增益值来验证 ChAHA（带有帐篷图）的性能，该控制器经过精心定制，以满足 MATLAB/ 中直流电机速度控制的特定要求。模拟链接。毫无疑问，采用所提出的 ChAHA-FOPID 控制器的闭环系统比某些现有控制器具有更好的性能，例如基于灰狼优化的 FOPID (GWO-FOPID)、基于原子搜索优化的 FOPID (ASO-FOPID) 和基于 FOPID (MRFO-FOPID) 控制器的蝠鲼觅食优化。最后，还对直流电机的参数变化进行了鲁棒性分析，最终的仿真结果验证了该方法的优越性。