Evaluation of Ant Colony Optimization Algorithm Compared to Genetic Algorithm, Dynamic Programming and Branch and Bound Algorithm Regarding Travelling Salesman Problem
Evaluation of Ant Colony Optimization Algorithm Compared to Genetic Algorithm, Dynamic Programming and Branch and Bound Algorithm Regarding Travelling Salesman Problem
Article PDF

Keywords

swarm intelligence
vehicle routing
ant colony optimization

How to Cite

A.H.M Saiful Islam, Mashrure Tanzim, Sadia Afreen, & Gerald Rozario. (2019). Evaluation of Ant Colony Optimization Algorithm Compared to Genetic Algorithm, Dynamic Programming and Branch and Bound Algorithm Regarding Travelling Salesman Problem. Global Journal of Computer Science and Technology, 19(D3), 7–12. Retrieved from https://gjcst.com/index.php/gjcst/article/view/511

Abstract

We use ant colony optimization ACO algorithm for solving combinatorial optimization problems such as the traveling salesman problem Some applications of ACO are vehicle routing sequential ordering graph coloring routing in communications networks etc In this paper we compare the performance of ACO to that of a few other state-of-the-art algorithms currently in use and thus measure the effectiveness of ACO as one of the major optimization algorithms in regard with a few more algorithms The performance of the algorithms is measured by observing their capacity to solve a traveling salesman problem TSP This paper will help to find the proper algorithm to be used for routing problems in different real-life situations
Article PDF
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2019 Authors and Global Journals Private Limited