Benchmark Tests on Heuristic Methods in the Darts Game

• Autorzy: Książek K. , Połap D. , Woźniak M.

Identyfikatory

DOI

10.24425/119358

• Języki publikacji: EN

Abstrakty

EN

Games are among problems that can be reduced to optimization, for which one of the most universal and productive solving method is a heuristic approach. In this article we present results of benchmark tests on using 5 heuristic methods to solve a physical model of the darts game. Discussion of the scores and conclusions from the research have shown that application of heuristic methods can simulate artificial intelligence as a regular player with very good results.

Słowa kluczowe

EN

metaheuristics; optimization; computational intelligence

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2018
• Tom: Vol. 64, No. 2
• Strony: 115--121
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Książek K.

• Institute of Mathematics, Silesian University of Technology, Poland

autor

Połap D.

• Institute of Mathematics, Silesian University of Technology, Poland

autor

Woźniak M.

• Institute of Mathematics, Silesian University of Technology, Poland

Bibliografia

• [1] M. Wlodarczyk-Sielicka, “Importance of neighborhood parameters during clustering of bathymetric data using neural network,” in Information and Software Technologies - 22nd International Conference, ICIST 2016, Druskininkai, Lithuania, October 13-15, 2016, Proceedings, 2016, pp. 441-452.
• [2] M. Wlodarczyk-Sielicka and A. Stateczny, “Clustering bathymetric data for electronic navigational charts,” The Journal of Navigation, vol. 69, no. 5, pp. 1143-1153, 2016.
• [3] L. Zhang, L. Wang, W. Lin, and S. Yan, “Geometric optimum experimental design for collaborative image retrieval,” IEEE Trans. Circuits Syst. Video Techn., vol. 24, no. 2, pp. 346-359, 2014.
• [4] J. Mandziuk and M. Swiechowski, “UCT in capacitated vehicle routing problem with traffic jams,” Inf. Sci., vol. 406, pp. 42-56, 2017.
• [5] L. S. Ezema and C. I. Ani, “Artificial neural network approach to mobile location estimation in gsm network,” International Journal of Electronics and Telecommunications, vol. 63, no. 1, pp. 39-44, 2017.
• [6] A. Esmaeili, N. Mozayani, M. J. Motlagh, and E. T. Matson, “The impact of diversity on performance of holonic multi-agent systems,” Eng. Appl. of AI, vol. 55, pp. 186-201, 2016.
• [7] A. Esmaeili, N. Mozayani, M. J. Motlagh, and E. T. Matson, “A socially-based distributed self-organizing algorithm for holonic multi-agent systems: Case study in a task environment,” Cognitive Systems Research, vol. 43, pp. 21-44, 2017.
• [8] Z. Marszalek, “Novel recursive fast sort algorithm,” in Information and Software Technologies - 22nd International Conference, ICIST 2016, Druskininkai, Lithuania, October 13-15, 2016, Proceedings, 2016, pp. 344-355.
• [9] Z. Marszałek, “Performance test on triple heap sort algorithm,” PUBLISHER UWM OLSZTYN 2017, vol. 20, no. 1, pp. 49-61, 2017.
• [10] K. Ksiazek, “First approach to solve linear system of equations by using ant colony optimization,” in Proceedings of the Symposium for Young Scientists in Technology, Engineering and Mathematics (SYSTEM 2017), CEUR Vol. 1853, Kaunas University of Technology, Lithuania 2017, 2017, pp. 57-61.
• [11] R. Brociek and D. Slota, “Application and comparison of intelligent algorithms to solve the fractional heat conduction inverse problem,” ITC, vol. 45, no. 2, pp. 184-194, 2016.
• [12] R. Brociek and D. Slota, “Application of real ant colony optimization algorithm to solve space and time fractional heat conduction inverse problem,” ITC, vol. 46, no. 2, pp. 171-182, 2017.
• [13] R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm intelligence, vol. 1, no. 1, pp. 33-57, 2007.
• [14] M. D. Toksari, “Ant colony optimization for finding the global minimum,” Applied Mathematics and Computation, vol. 176, no. 1, pp. 308-316, 2006.
• [15] J. Brownlee, “Clonal selection theory & clonalg-the clonal selection classification algorithm (csca),” Swinburne University of Technology, 2005.
• [16] L. N. De Castro and F. J. Von Zuben, “Learning and optimization using the clonal selection principle,” IEEE transactions on evolutionary computation, vol. 6, no. 3, pp. 239-251, 2002.
• [17] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
• [18] S. Mirjalili, “The ant lion optimizer,” Advances in Engineering Software, vol. 83, pp. 80-98, 2015.
• [19] K. Ksiazek, W. Masarczyk, and I. Nowak, “Heuristic approach to the game of darts by using genetic algorithm and ant colony optimization,” in Proceedings of the International Conference for Young Researchers in Informatics, Mathematics and Engineering (ICYRIME 2017), CEUR Vol. 1852, Kaunas University of Technology, Lithuania 2017, 2017, pp. 33-38.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).