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A comparative analysis of genetic algorithms on a case study of asymmetric traveling salesman problem

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Abstract

In the present paper, the genetic algorithm and some of its variants i.e. adaptive genetic algorithm, binary-coded genetic algorithm and real-coded genetic algorithm are applied to the Asymmetric Traveling Salesman Problem (ATSP). ATSP is one of the most widely studied combinatorial NP-hard problems of finding the shortest path. The present ATSP is a novel real-life case of the shortest path problem based on the distances between 22 districts of Haryana, India. To solve the above problem, one-point crossover and exchange mutation are applied to compare the performance of these algorithms on different parameters such as the size of the population, the number of iterations, and the rate of crossover. The main objective of this paper is to study the influence of these parameters on ATSP. Numerical results show that the binary genetic algorithm worked better in terms of the size of the population and the number of iterations, while the real-coded genetic algorithm worked better in terms of the rate of crossover.

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Acknowledgements

The first author is extremely grateful to the Council of Scientific & Industrial Research (CSIR) for providing Junior Research Fellowship (JRF) with file number 09/1152(0024)/2020-EMR-1 and encouragement, which made this research possible.

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Authors and Affiliations

  1. Department of Mathematics, Central University of Haryana, Jant-Pali, Mahendergarh, Haryana, 123031, India

    Amit Raj, Parul Punia & Pawan Kumar

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  1. Amit Raj
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  2. Parul Punia
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  3. Pawan Kumar
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Correspondence to Pawan Kumar.

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Raj, A., Punia, P. & Kumar, P. A comparative analysis of genetic algorithms on a case study of asymmetric traveling salesman problem. Int J Syst Assur Eng Manag 14, 2684–2694 (2023). https://doi.org/10.1007/s13198-023-02161-2

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  • DOI: https://doi.org/10.1007/s13198-023-02161-2

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