Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression

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 appr...

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Bibliographic Details
Published in:Journal of Applied Mathematics 2014-01, Vol.2014 (2014), p.86-95-807
Main Authors: Akandwanaho, Stephen M., Adewumi, Aderemi Oluyinka, Adebiyi, Ayodele Ariyo
Format: Article
Language:English
Subjects:
Dynamics
Gaussian
Heuristic
Mathematical analysis
Mathematical models
Neural networks
Optimization
Regression
Studies
Tours
Traveling salesman problem
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Summary: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.
ISSN:1110-757X
1687-0042
DOI:10.1155/2014/818529