Abstract:

In order to reduce repeated exploration and improve population diversity and spatial search ability of Flower Pollination Algorithm （FPA）， a Flower Pollination Algorithm based on Neural Network optimization （NNFPA） was proposed. In the algorithm， an adaptive control factor was used to switch the global search and local search dynamically. The global search strategy of multi-party information was employed to speed up the convergence and maintain the diversity of pollen population， as well as reduce the dependence of population on social attributes in later iterations of the algorithm. The local search strategy based on neural networks was used to enable the algorithm to have memory function， so that the algorithm was able to have a stable search strategy， thereby reducing the uncertainty of the algorithm and allowing it to explore the solution space more fully. Nine common test functions and some functions selected from CEC2014 test set were chosen for simulation. The results show that compared with the standard FPA and the variant algorithm Flower Pollination Algorithm based on Hybrid Strategy （HSFPA）， NNFPA achieves higher search accuracy and convergence speed on the chosen test functions. It can be seen that NNFPA has better optimization ability.

Key words: Flower Pollination Algorithm (FPA), adaptive, diversity, neural network, memory function

摘要：

为了降低花朵授粉算法（FPA）重复探索的情况，并提高算法的种群多样性和空间搜索能力，提出一种基于神经网络优化的花朵授粉算法（NNFPA）。设定自适应控制因子，从而动态地切换全局与局部搜索；利用多方信息的全局搜索策略提高算法收敛速度并维持花粉种群的多样性，同时减少在算法迭代后期种群对社会属性的依赖；基于神经网络的局部搜索策略让算法具有记忆功能，这样算法就能具有稳定搜索策略，从而降低算法的不确定性，使它能更充分地探索解空间。选取9个常规测试函数与CEC2014测试集中的部分函数进行仿真实验，得到的结果表明：与标准FPA以及变种算法HSFPA（FPA based on Hybrid Strategy）相比，NNFPA在所选测试函数上具有较高的搜索精度和收敛速度。可见NNFPA具有更好的寻优能力。

关键词: 花朵授粉算法, 自适应, 多样性, 神经网络, 记忆功能