Peter Grabusts, Jurijs Musatovs


The aim of the paper is to popularize the Razna National Park’s tourist attractions. The opportunity to choose the shortest route to visit all the most interesting potential sightseeing objects is offered. The authors continue their research on the theoretical and practical aspects of searching for the shortest route. Theoretical research has been carried out and mathematically the shortest route has been calculated for various sightseeing objects of the Razna National Park. The paper also provides mapping of these objects and an analysis of the locations of the sightseeing objects at different levels. The main goal of the paper is to show the possibilities of applying mathematical models in solving practical tasks – to determine the shortest route between the sightseeing objects. This research describes an optimization method called Simulated Annealing. The Simulated Annealing method is widely used for various combinatorial optimization tasks. Simulated Annealing is a stochastic optimization method that can be used to minimize the specified cost function given a combinatorial system with multiple degrees of freedom. In this paper, the application of the Travelling Salesman Problem is demonstrated, and an experiment aimed to find the shortest route between the Razna National Park sightseeing objects is performed. Common research methods are used in this research: the descriptive research method, the statistical method, mathematical modelling.


optimization; Razna National Park; Simulated Annealing; tourism objects; Travelling Salesman Problem

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APPLEGATE, D.L., BIXBY, R., CHVÁTAL, V., COOK, W. (2006). The Traveling Salesman Problem: A Computational Study. Princeton University Press.

COOK, W. (2011). In Pursuit of the Traveling Salesman. Princeton: Princeton University Press, USA.

COUGHLIN, J.P., BARAN, R.H. (1985). Neural Computation in Hopfield Networks and Boltzmann Machines. University of Delaware Press.

Dabas aizsardzības pārvalde (2018, April 10). Retrieved from (in Latvian).

GRABUSTS, P. (2000). Solving TSP using classical simulated annealing method. Scientific Proceedings of Riga Technical University: Computer Science. Information Technology and Management Science, RTU, Riga, Issue 5, Volume 2, pp. 32-39.

GRABUSTS, P., MUSATOVS, J. (2017). Optimal Route Detection Between Educational Institutions of Rezekne Municipality. Latgale National Economy Research. Journal of Social Sciences, No. 1(9), pp.25-34.

GRANVILLE, V., KRIVANEK, M., RASSON J-P. (1994). Simulated annealing: A proof of convergence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), pp. 652–656.

INGBER, L. (1993). Simulated annealing: Practice versus theory. Math. Comput. Modelling, 18, pp. 29–57.

KIRKPATRICK, S., GELATT, C.D., VECCHI, M.P. (1983). Optimization by Simulated Annealing. Science, vol. 220, pp. 671-680.

LAARHOVEN, P.J.M., AARTS, E.H.L. (1987). Simulated Annealing: Theory and Applications. D.Reidel Publishing Company, Holland.

OTTEN, R.H., GINNEKEN, L.P. (1989). The Annealing Algorithm. Kluwer Academic Publishers.

Razna National Park (2018, April 10). Retrieved from en/sight/razna-national-park.



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