Abstract: Aiming at the shortages of basic flower pollination algorithm with easy to fall into local optimum, slow convergence speed and low search precision, a new algorithm based on dynamic adjustment and cooperative search（FPADC） is proposed. Halton sequences are used to enhance the quality of initial solution. Through the division of population, the diversity of the population can be improved to jump out of the local optimum. In order to improve the convergence rate and precision, optimizing strategy of the algorithms is dynamically adjusted according to the population evolutionary information. The experimental results show that the proposed algorithm is much better than basic FPA and its several improved algorithms in optimization performance.

Key words: flower pollination algorithm, swarm intelligent algorithm, Gauss mutation, dynamic tuning

摘要： 针对基本花授粉算法（Flower Pollination Algorithm，FPA）容易陷入局部最优、收敛速度慢及寻优精度低等缺陷，提出了基于动态调整和协同搜索的花授粉算法（Flower Pollination Algorithm based on Dynamic adjustment and Cooperative search，FPADC）。利用霍尔顿序列提升初始解的质量；通过对种群进行分工，从而提高种群的多样性以跳出局部最优；根据种群进化信息动态调整算法的寻优策略，从而提高收敛速度和精度。仿真实验结果表明，提出的改进算法相比基本花授粉算法和部分改进算法，有较好的寻优性能。

关键词: 花授粉算法, 群智能算法, 高斯变异, 动态调整