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Refined descriptive sampling simulated annealing algorithm for solving the traveling salesman problem

Author

Listed:
  • Cherabli Meriem
  • Boubalou Meriem

    (Centre Universitaire Morsli Abdellah de Tipaza, 42000, Tipaza, ; and Laboratoire de Mathématiques Appliqués, FSE, Université de Bejaia, 06000, Bejaia, Algeria)

  • Ourbih-Tari Megdouda

    (Centre Universitaire Morsli Abdellah de Tipaza, 42000, Tipaza, ; and Laboratoire de Mathématiques Appliqués, FSE, Université de Bejaia, 06000, Bejaia, Algeria)

Abstract

The simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. In this paper, we propose a software component under the Windows environment called goRDS which implements a refined descriptive sampling (RDS) number generator of high quality in the MATLAB programming language. The aim of this generator is to sample random inputs through the RDS method to be used in the Simple SA algorithm with swap operator. In this way, the new probabilistic meta-heuristic algorithm called RDS-SA algorithm will enhance the simple SA algorithm with swap operator, the SA algorithm and possibly its variants with solutions of better quality and precision. Towards this goal, the goRDS generator was highly tested by adequate statistical tests and compared statistically to the random number generator (RNG) of MATLAB, and it was proved that goRDS has passed all tests better. Simulation experiments were carried out on the benchmark traveling salesman problem (TSP) and the results show that the solutions obtained with the RDS-SA algorithm are of better quality and precision than those of the simple SA algorithm with swap operator, since the software component goRDS represents the probability behavior of the SA input random variables better than the usual RNG.

Suggested Citation

  • Cherabli Meriem & Boubalou Meriem & Ourbih-Tari Megdouda, 2022. "Refined descriptive sampling simulated annealing algorithm for solving the traveling salesman problem," Monte Carlo Methods and Applications, De Gruyter, vol. 28(2), pages 175-188, June.
    • Handle: RePEc:bpj:mcmeap:v:28:y:2022:i:2:p:175-188:n:9
    DOI: 10.1515/mcma-2022-2113
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