中文摘要：

受自然界花朵授粉过程的启发，Yang提出了一种新的元启发式群智能算法——花朵授粉算法，该算法融合了现有其他智能算法的优点。首先阐述了花朵授粉的特征，从机理上描述了该算法的实现步骤，同时对该算法的寻优性能进行了剖析。其次，针对花朵授粉算法寻优精度低、收敛速度慢、易陷入局部极小的不足，提出一种基于差分进化策略的花朵授粉算法，该算法引入差分进化中的变异、交叉及选择操作，使缺乏变异机制的花朵授粉算法具有变异能力，增加种群的多样性，提高算法的全局寻优能力和避免种群个体陷入局部最优。通过十个标准测试函数进行测试，仿真结果表明，改进算法的寻优能力明显优于基本的花朵授粉算法、蝙蝠算法、粒子群算法及改进的粒子群算法。

英文摘要：

Inspired process of flower pollination in the nature world, Yang proposed a new metaheuristic intelligent algorithms- flower pollination algorithm, this algorithm combined with virtues of the existing other intelligent algorithms. Firstly, this paper expounded the features of flower pollination, described the implementation steps of the algorithm from the mechanism, and analyzed the optimization performance of the algorithm. Secondly, this paper presented the flower pollination algorithm based on differential evolution strategy to overcome the low accuracy computation, slow speed convergence, and it was easy to fall in- to local optimization. The algorithm introduced deviation, crossover and selection operation from the differential evolution, the flower pollination algorithm of lacking variation mechanism was variable capacity, which increased the diversity of population, improved the global searching ability of the algorithm and avoided falling into local optimum population. This paper utilized ten standard test functions to test the algorithm, the simulation results show that optimization ability of the improved algorithm is significantly better than those of the basic flower pollination algorithm, bat algorithm, particle swarm algorithm and improved particle swarm optimization algorithm.