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; Mashrure Tanzim; Sadia Afreen; Gerald Rozario

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 * §

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Mashrure Tanzim

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Sadia Afreen

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Gerald Rozario

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§ Notre Dame University Bangladesh

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

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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.

References

6 Cites in Article

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M Abid,I Muhammad (2015). Heuristic Approaches to Solve Traveling Salesman Problem.
D Davendra (2010). Traveling Salesman Problem, Theory and Applications. INTECH open access publishers.
Dirk Sudholt,Christian Thyssen (2012). Running time analysis of Ant Colony Optimization for shortest path problems.
Marco Dorigo,Mauro Birattari,Thomas Stutzle (2006). Ant Colony Optimization.
B Raghavendra (2015). Solving Traveling Salesmen Problem using Ant Colony Optimization Algorithm.
Wang Hui (2012). Comparison of several intelligent algorithms for solving TSP problem in industrial engineering.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

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 - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D3).

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GJCST Volume 19 Issue D3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification F.2.2

Submission ReceivedDecember 8, 2018
Peer Review Double Blind
Handling Editor
Accepted January 2, 2019
Published January 15, 2019

Version of record

v1.2

Issue date

July 17, 2019

Language

English

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Article Matrices

Total Score: 144

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology

Authors: A.H.M Saiful Islam, Mashrure Tanzim, Sadia Afreen, Gerald Rozario (PhD/Dr. count: 0)

View Count (all-time): 418

Total Views (Real + Logic): 2051

Total Downloads (simulated): 139

Publish Date: 2019 01, Tue

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Total Views: 2.1K

Total Downloads: 139

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