JOURNAL ARTICLE

An improved genetic algorithm for solving the Traveling Salesman Problem

Abstract

In this paper, on the basis of the original genetic algorithm, an improved genetic algorithm for the Traveling Salesman Problem (TSP) is proposed. Firstly, the diversity of species is ensured by amending the calculation method of the individual fitness. Secondly, the mutation operator is improved by the combination of shift mutation and insertion mutation. Before the crossover, the operator checks whether the degradation phenomenon will occur. Finally, experimental results further determine that above improvements provide a significant effect for solving the TSP.

Keywords:
Travelling salesman problem Crossover Mutation Genetic algorithm Operator (biology) Mathematical optimization 2-opt Computer science Genetic operator Bottleneck traveling salesman problem Algorithm Mathematics Population-based incremental learning Artificial intelligence Biology

Metrics

17
Cited By
0.47
FWCI (Field Weighted Citation Impact)
20
Refs
0.78
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Metaheuristic Optimization Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Multi-Objective Optimization Algorithms
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
Scheduling and Timetabling Solutions
Social Sciences →  Decision Sciences →  Management Science and Operations Research

Related Documents

JOURNAL ARTICLE

Improved Ant Colony Genetic Algorithm for Solving Traveling Salesman Problem

Wenming WangJiangdong ZhaoJi Huang

Journal:   Journal of Physics Conference Series Year: 2020 Vol: 1693 Pages: 012085-012085
JOURNAL ARTICLE

Improved Grouping Genetic Algorithm for Solving Multiple Traveling Salesman Problem

Yongzhen WangYan ChenYingying Yu

Journal:   电子与信息学报 Year: 2017 Vol: 39 (1)Pages: 198-205
JOURNAL ARTICLE

Improved Quantum Crossover Based Genetic Algorithm For Solving Traveling Salesman Problem

Yang YuHui Li

Journal:   International Journal of Advancements in Computing Technology Year: 2013 Vol: 5 (1)Pages: 651-658
© 2026 ScienceGate Book Chapters — All rights reserved.