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Journal of Applied Mathematics
Volume 2014, Article ID 818529, 10 pages
Research Article

Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression

School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, University Road, Westville, Private Bag X 54001, Durban, 4000, South Africa

Received 4 January 2014; Accepted 11 February 2014; Published 7 April 2014

Academic Editor: M. Montaz Ali

Copyright © 2014 Stephen M. Akandwanaho et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.