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Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression

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  • Stephen M. Akandwanaho
  • Aderemi O. Adewumi
  • Ayodele A. Adebiyi

Abstract

This paper solves the dynamic traveling salesman problem (DTSP) using dynamic Gaussian Process Regression (DGPR) method. The problem of varying correlation tour is alleviated by the nonstationary covariance function interleaved with DGPR to generate a predictive distribution for DTSP tour. This approach is conjoined with Nearest Neighbor (NN) method and the iterated local search to track dynamic optima. Experimental results were obtained on DTSP instances. The comparisons were performed with Genetic Algorithm and Simulated Annealing. The proposed approach demonstrates superiority in finding good traveling salesman problem (TSP) tour and less computational time in nonstationary conditions.

Suggested Citation

  • Stephen M. Akandwanaho & Aderemi O. Adewumi & Ayodele A. Adebiyi, 2014. "Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:818529
    DOI: 10.1155/2014/818529
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    References listed on IDEAS

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    1. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
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