基于Spark的并行遗传算法在物流配送问题中的应用

传统的遗传算法在数据量不足的单机情况下可能存在早熟的现象，遗传算法对搜索范围的依赖性很强，大搜索范围的遗传算法往往有更好的表现。为解决以上问题，可把Spark海量存储和并行计算的能力运用到遗传算法的求解上，实现一种粗粒度的并行遗传算法。利用Spark并行执行遗传算法的选择、交叉和变异等操作，可以大大提高遗传算法的搜索范围和执行速度。实验将改进后的遗传算法应用到物流配送问题中，结果表明，与单机和传统的并行模型相比，基于Spark的遗传算法在运行时间上明显减少，同时早熟的现象也得到了缓解。

Application of Parallel Genetic Algorithm Based on Spark in Logistics Distribution

The traditional genetic algorithm may be premature in the case of insufficient data, it has a strong dependence on the search range, and the genetic algorithm with large search range often has a better performance. In order to solve the above problems, we can use the Spark mass storage and parallel computing ability to solve the genetic algorithm, implementing a coarse-grained parallel model, executing genetic algorithm selection, crossover and mutation operations in parallel using Spark. Executing genetic algorithm in parallel with Spark can greatly improve the search scope and parallel running speed. The application of improved genetic algorithm to the logistics and distribution problems and the experimental results show that compared with the serial and traditional parallel program, running time of genetic algorithm based on Spark is significantly reduced, and premature phenomenon is eased also.