phat 19(19): e4

Research Article

Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB

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  • @ARTICLE{10.4108/eai.13-7-2018.163092,
        author={Kalaipriyan Thirugnanasambandam and Raghav. R.S and Saravanan. D and Prabu. U and Rajeswari. M},
        title={Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={5},
        number={19},
        publisher={EAI},
        journal_a={PHAT},
        year={2020},
        month={2},
        keywords={Ant Colony Optimization, Ant System, Travelling Salesman Problem, TSPLIB},
        doi={10.4108/eai.13-7-2018.163092}
    }
    
  • Kalaipriyan Thirugnanasambandam
    Raghav. R.S
    Saravanan. D
    Prabu. U
    Rajeswari. M
    Year: 2020
    Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB
    PHAT
    EAI
    DOI: 10.4108/eai.13-7-2018.163092
Kalaipriyan Thirugnanasambandam1, Raghav. R.S2,*, Saravanan. D3, Prabu. U4, Rajeswari. M5
  • 1: Department of Computer Science and Technology, School of Computers, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India
  • 2: School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India
  • 3: Department of Computer Science and Engineering, KL Deemed to be University, Vaddeswaram, Guntur, Andhra Pradesh, India
  • 4: Department of CSE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
  • 5: Sri Manakula Vinayagar Engineering College, Puducherry, India
*Contact email: vpmrags@gmail.com

Abstract

INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as sub problems in many fields apart from computer science and also in the field of computer science.

OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant Colony Optimization.

METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP). The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, its mean, median, variance and the standard deviation.

RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and the deviation of the solutions from the TSPLIB provides best known solutions. For instances, TSPLIB data sets have been used.

CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results are analysed in terms of different parameter settings and performance metrics on each of it. The role of parameters has also been discussed along with the experimental results.