Multi-chromosome Genetic Algorithm for Multiple Traveling Salesman Problem

Authors

Duofu Ye, Rocket Force University of Engineering, Xi’an 710025, China;
Liu Gang, Rocket Force University of Engineering, Xi’an 710025, China;
He Bing, Rocket Force University of Engineering, Xi’an 710025, China;

Abstract

Abstract: A multi-traveling salesman model with time window is established, and two objective functions for the number of traveling salesmen and the sum of travel time are designed. A multi - chromosome coding method is designed to develop complex mutation operator tree, which overcomes the problem of large searching space of traditional genetic algorithms. The performances of algorithms are compared by simulation, and the simulation results show that the genetic algorithm with complex multi-chromosome mutation tree can balance the two objective functions of the number of TSP and total travel time well, improve the algorithm of travel speed, and reduce the total travel time by 15.8%.

Recommended Citation

Ye, Duofu; Gang, Liu; and Bing, He (2019) "Multi-chromosome Genetic Algorithm for Multiple Traveling Salesman Problem," Journal of System Simulation: Vol. 31: Iss. 1, Article 5.
DOI: 10.16182/j.issn1004731x.joss.17-0046
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss1/5

First Page

36

Revised Date

2017-05-04

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-0046

Last Page

42

CLC

TP18

Recommended Citation

Ye Duofu, Liu Gang, He Bing. Multi-chromosome Genetic Algorithm for Multiple Traveling Salesman Problem[J]. Journal of System Simulation, 2019, 31(1): 36-42.

DOI

10.16182/j.issn1004731x.joss.17-0046