基于动态全局搜索和柯西变异的花授粉算法

针对基本花授粉算法（FPA）收敛速度慢、寻优精度低以及容易陷入局部最优的缺点，提出了一种基于动态全局搜索和柯西变异的花授粉算法DCFPA。利用混沌映射增强花粉种群初始分布的随机性和均匀性，在全局授粉过程中，引入全局平均最优花粉位置和动态权重递减因子共同实现花粉个体位置的更新，牵引算法朝着正确的搜索方向进行，避免算法早熟收敛，最后利用Cauchy变异，增加种群多样性，帮助算法跳出局部最优。对6个测试函数进行仿真实验表明，DCFPA算法比FPA具有更好的全局优化能力，提升了算法的收敛速度与求解精度；与相关的改进算法比较结果也表明，DCFPA整体上也具有更好的优化性能。

Flower Pollination Algorithm Based on Dynamic Global Search and Cauchy Mutation

Aiming at the shortages of basic Flower Pollination Algorithm（FPA） with slow convergence speed, low search precision and easy to fall into local optimum, a new algorithm based on dynamic global search and Cauchy mutation DCFPA is proposed. Firstly, it uses chaotic map to enhance the randomness and uniformity of the initial distribution of pollen population. Then, global average pollen position and dynamic weight reduction factor are introduced to achieve the update of individual pollen location in the process of global pollination, which can guide algorithm to correct search direction and avoid premature convergence. Finally, Cauchy mutation is used to increase the population diversity and help the algorithm to jump out of the local optimum. The simulation experiments on six classical test functions show that compared with FPA, DCFPA algorithm has better global optimization ability, and improves the convergence speed and solution accuracy of the algorithm and also has better optimization performance than those improved algorithm in related literatures.