An application study on vehicle routing problem based on improved genetic algorithm

Authors:
Shang Huang;Xiufen Fu;Peiwen Chen;CuiCui Ge;Shaohua Teng
Affiliations:
School of Computer, Guangdong University of Technology, Guangzhou, China;School of Computer, Guangdong University of Technology, Guangzhou, China;School of Computer, Guangdong University of Technology, Guangzhou, China;School of Computer, Guangdong University of Technology, Guangzhou, China;School of Computer, Guangdong University of Technology, Guangzhou, China
Venue:
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Year:
2012

Citing 2
Cited 0

Bio-inspired algorithms for autonomous deployment and localization of sensor nodes

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Vehicle Routing with Driver Learning for Real World CEP Problems

HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Vehicle Routing Problem of Logistics and Distribution is a hot and difficult issue in current field of combinatorial optimization, therefore this paper presents an improved genetic algorithm. The algorithm which applied the idea of Saving Algorithm to the initialization of groups, and improved algorithm on selection operator and cross operator, In the meantime, it proposes a new way to calculate the adaptive probability in the cross operator. In addition, it also introduces a novel CX crossover operator .By the way of simulating experiments of the Vehicle Routing Problem, it demonstrates that the improved genetic algorithm enhanced the ability of global optimization, moreover it can significantly speed up convergence efficiency.