基于随机替换和多样性控制的花朵授粉算法

针对花朵授粉算法收敛速度慢、不易跳出局部最优、搜索精度低的问题，根据花朵授粉算法的运动特点，提出了中心随机替换策略，加快算法的搜索速度。同时为了提高算法的全局搜索能力，加入多样性控制策略，动态改变转换概率p]，增加全局搜索的概率。经六种测试函数仿真实验，该算法在加快收敛速度的同时，合理地保持算法的多样性，相比于其他启发式智能算法拥有更快的寻优速度和更好的寻优精度。

Flower Pollination Algorithm Based on Random Substitution and Diversity Control

Focused on the convergence rate of flower pollination algorithm is slow and is not easy to jump out of local optimum and has low search accuracy, according to the trajectory of flower pollination algorithm, the center random replacement strategy is proposed, to accelerate the speed of the search algorithm. At the same time, to improve the global search ability of the algorithm, the diversity control strategy is added to change the transition probability p] dynamically, and the probability of global search is increased. Through the simulation experiments of 6 kinds of test functions, the algorithm reasonably maintains the diversity of the algorithm. Compared with other intelligent algorithms, the algorithm can have faster search speed and better optimization accuracy.