A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows.
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following.
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China. xushh@casm.ac.cn.
Sheng-Hua
SH
Xu
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China. liujip@casm.ac.cn.
Ji-Ping
JP
Liu
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China. zhangfh@casm.ac.cn.
Fu-Hao
FH
Zhang
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China. wangl@casm.ac.cn.
Liang
L
Wang
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China. sunlj@casm.ac.cn.
Li-Jian
LJ
Sun
VRPTW; genetic; particle swarm optimization; vehicle routing problem