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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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/818529 |
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| author | Stephen M. Akandwanaho Aderemi O. Adewumi Ayodele A. Adebiyi |
| author_facet | Stephen M. Akandwanaho Aderemi O. Adewumi Ayodele A. Adebiyi |
| author_sort | Stephen M. Akandwanaho |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9e438cc5c6fa4e44b3dce1edfe3dca09 |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-9e438cc5c6fa4e44b3dce1edfe3dca092025-08-20T02:18:32ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/818529818529Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process RegressionStephen M. Akandwanaho0Aderemi O. Adewumi1Ayodele A. Adebiyi2School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, University Road, Westville, Private Bag X 54001, Durban, 4000, South AfricaSchool of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, University Road, Westville, Private Bag X 54001, Durban, 4000, South AfricaSchool of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, University Road, Westville, Private Bag X 54001, Durban, 4000, South AfricaThis 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.http://dx.doi.org/10.1155/2014/818529 |
| spellingShingle | Stephen M. Akandwanaho Aderemi O. Adewumi Ayodele A. Adebiyi Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression Journal of Applied Mathematics |
| title | Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression |
| title_full | Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression |
| title_fullStr | Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression |
| title_full_unstemmed | Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression |
| title_short | Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression |
| title_sort | solving dynamic traveling salesman problem using dynamic gaussian process regression |
| url | http://dx.doi.org/10.1155/2014/818529 |
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