采用混合搜索策略的阿奎拉优化算法

针对阿奎拉优化算法（AO）存在的不足，提出一种采用混合搜索策略的阿奎拉优化算法（HAO）。首先，利用动态调整函数平衡算法的全局探索与局部开发；其次，利用混沌自适应权重来增强算法的全局搜索能力、加快算法的收敛速度；最后，设计新的个体变异概率系数，采用改进型差分变异策略，利用适应度值较优个体引领群体中其他个体开展搜索活动，保持了种群的多样性，增强了算法跳出局部最优能力。通过八个基准测试函数和10个CEC2019测试函数，以及一个工程应用问题的数值实验仿真对所提算法进行实验验证。实验结果表明，所提算法的全局收敛速度和优化精度均得到了明显地改善，跳出局部最优的能力得到了增强。

Aquila optimization algorithm using hybrid search strategies

Aiming at the shortcomings of aquila optimization algorithm(AO), this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation. Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm. Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. The proposed algorithm was verified by experiments on the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem. The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum.