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Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression

Akandwanaho, S. M. and Adewumi, O. A. and Adebiyi, A. A. (2014) Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression. Journal of Applied Mathematics, 2014. pp. 1-10.

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mr Adewole Adewumi
Date Deposited: 20 Mar 2015 16:49
Last Modified: 20 Mar 2015 16:49
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/4116

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