Bioinformation Heuristic Genetic Algorithm for Solving TSP

Authors

Jia Xu, Chongqing Jiaotong University, Chongqing 400046, China;Follow
Fengqing Han, Chongqing Jiaotong University, Chongqing 400046, China;Follow
Qixin Liu, Chongqing Jiaotong University, Chongqing 400046, China;
Xiaoxia Xue, Chongqing Jiaotong University, Chongqing 400046, China;

Abstract

Abstract: Genetic algorithm (GA) is one of the universal path optimization algorithms for traveling salesman problem (TSP). Aiming at the slow convergence and unstable solution of the traditional GA, a bioinformation heuristic genetic algorithm (BHGA) is proposed. By optimizing the fitness function and initial population, the gene sequence comparison technique in bioinformatics is introduced to carry out the cross recombination sorting. The gene reversal operation is used to implement mutation, to accelerate the convergence speed and get a better path solution. The numerical examples in TSPLIB database are solved by BHGA and the experimental simulation results show that the algorithm is effective and the solution of the medium and small scale TSP data are stable.

Recommended Citation

Xu, Jia; Han, Fengqing; Liu, Qixin; and Xue, Xiaoxia (2022) "Bioinformation Heuristic Genetic Algorithm for Solving TSP," Journal of System Simulation: Vol. 34: Iss. 8, Article 14.
DOI: 10.16182/j.issn1004731x.joss.21-0203
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss8/14

First Page

1811

Revised Date

2021-06-10

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0203

Last Page

1819

CLC

TP391.9

Recommended Citation

Jia Xu, Fengqing Han, Qixin Liu, Xiaoxia Xue. Bioinformation Heuristic Genetic Algorithm for Solving TSP[J]. Journal of System Simulation, 2022, 34(8): 1811-1819.

Corresponding Author

Fengqing Han,990020606030@cqjtu.edu.cn

DOI

10.16182/j.issn1004731x.joss.21-0203