摘要
为了提高物流需求预测的准确性,针对现有遗传算法存在的局部最优解等问题,提出了改进遗传BP算法。首先,采用了双种群进化机制;其次,通过双极性压缩函数对适应度函数值进行改进;然后,设计了基于排挤方法的选择算子;最后,利用改进遗传算法对BP神经网络的初始权值和阀值进行优化。仿真实验表明,文章所提算法能够更加准确地对物流需求进行预测。
In order to improve the accuracy of logistics demand forecasting, for the local optimal solution of the existing genetic algorithm, a logistics forecasting algorithm based on improved genetic algorithm was proposed. Firstly, the double population evolution mechanism was adopted; secondly, the fitness function value was improved by the bipolar compression function; then, the selection operator based on crowding method was designed; finally, the initial weights and threshold of BP neural network was optimized by improved genetic algorithm. Simulation experiments show that the proposed algorithm can more accurately forecast the logistics demand.
出处
《物流科技》
2015年第11期107-109,共3页
Logistics Sci-Tech
关键词
BP神经网络
遗传算法
物流需求
BP neural network
genetic algorithm
logistics demand