Applying Improved Genetic Algorithm to Solve Travelling Salesman Problem

Mayank Agrawal, Vinod Jain

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Travelling Salesman Problem is a very popular problem in graph theory and it is applicable to solve many problems of science and engineering. It has its applications in electronics, transportation, navigation, computer networks etc. As the number of cities increases the complexity of the algorithm to find the optimal solution increases. TSP is a maximization optimization problem. In this paper genetic algorithm is used to solve TSP problem. Genetic Algorithm is a nature inspired algorithm which is very suitable to solve optimization problems. An improved genetic algorithm is proposed and applied on standards TSP instances. The achieved results are found better as compared to other recent research works in this area.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020
PublisherInstitute of Electrical and Electronics Engineers
Pages1194-1197
Number of pages4
ISBN (Electronic)9781728153742
DOIs
StatePublished - 1 Jul 2020
Externally publishedYes
Event2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020 - Coimbatore, India
Duration: 15 Jul 202017 Jul 2020

Publication series

NameProceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020

Conference

Conference2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020
Country/TerritoryIndia
CityCoimbatore
Period15/07/2017/07/20

Keywords

  • Genetic Algorithm
  • NP-Hard Problem
  • Travelling Salesman Problem

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Decision Sciences (miscellaneous)
  • Information Systems and Management

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