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

Article ID

F8R2P

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

A.H.M Saiful Islam
A.H.M Saiful Islam
Mashrure Tanzim
Mashrure Tanzim Bangladesh University of Professionals
Sadia Afreen
Sadia Afreen
Gerald Rozario
Gerald Rozario
DOI

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.

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

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.

A.H.M Saiful Islam
A.H.M Saiful Islam
Mashrure Tanzim
Mashrure Tanzim Bangladesh University of Professionals
Sadia Afreen
Sadia Afreen
Gerald Rozario
Gerald Rozario

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A H M Saiful Islam. 2019. “. Global Journal of Computer Science and Technology – D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D3): .

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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-D Classification: F.2.2
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Evaluation of Ant Colony Optimization Algorithm Compared to Genetic Algorithm, Dynamic Programming and Branch and Bound Algorithm Regarding Travelling Salesman Problem

A.H.M Saiful Islam
A.H.M Saiful Islam
Mashrure Tanzim
Mashrure Tanzim Bangladesh University of Professionals
Sadia Afreen
Sadia Afreen
Gerald Rozario
Gerald Rozario

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