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Novel Discrete Rao Algorithms for Solving the Travelling Salesman Problem

Novel Discrete Rao Algorithms for Solving the Travelling Salesman Problem

Ankit Kumar Nikum
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 18
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781799860785|DOI: 10.4018/IJAEC.2021070104
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MLA

Nikum, Ankit Kumar. "Novel Discrete Rao Algorithms for Solving the Travelling Salesman Problem." IJAEC vol.12, no.3 2021: pp.44-61. http://doi.org/10.4018/IJAEC.2021070104

APA

Nikum, A. K. (2021). Novel Discrete Rao Algorithms for Solving the Travelling Salesman Problem. International Journal of Applied Evolutionary Computation (IJAEC), 12(3), 44-61. http://doi.org/10.4018/IJAEC.2021070104

Chicago

Nikum, Ankit Kumar. "Novel Discrete Rao Algorithms for Solving the Travelling Salesman Problem," International Journal of Applied Evolutionary Computation (IJAEC) 12, no.3: 44-61. http://doi.org/10.4018/IJAEC.2021070104

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Abstract

Rao algorithms are population-based metaphor-less optimization algorithms. Rao algorithms consist of three algorithms characterized by three mathematical equations. These algorithms use the characteristics of the best and worst solution to modify the current population along with some characteristics of a random solution. These algorithms are found to be very efficient for continuous optimization problems. In this paper, efforts are made to convert Rao 1 algorithm to its discrete form. This paper proposes three techniques for converting these continuous Rao algorithms to their discrete form. One of the techniques is based on swap operator used for transforming PSO to discrete PSO, and the other two techniques are based on two novel mutating techniques. The algorithms are applied to symmetric TSP problems, and the results are compared with different state of the art algorithms, including discrete bat algorithm (DBA), discrete cuckoo search (DCS), ant colony algorithm, and GA. The results of Rao algorithms are highly competitive compared to the rest of the algorithms

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