Abstract
In recent years a large number of evolutionary and other population based heuristics were proposed in the literature for solving NP-hard optimization problems. In 2015 we presented a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for The Traveling Salesman Problem. It provided results tested on series of TSP problems. In this paper we present an improved version of the DBMEA algorithm, where the local search is accelerated, which is the most time consuming part of the original DBMEA algorithm. This modification led to a significant improvement, the runtime of the improved DBMEA was 5–20 times shorter than the original DBMEA algorithm. Our DBMEA algorithms calculate real value costs better than integer ones, so we modified the Concorde algorithm be comparable with our results. The improved DBMEA was tested on several TSPLIB benchmark problems and other VLSI benchmark problems and the following values were compared: - optima found by the improved DBMEA heuristic and by the modified Concorde algorithm with real cost values - runtimes of original DBMEA, improved DBMEA and modified Concorde algorithm. Based on the test results we suggest the use of the improved DBMEA heuristic for the more efficient solution of TSP problems.
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References
Applegate, D.L., Bixby, R.E., Chvátal, V., Cook, W.J.: The Traveling Salesman Problem: A Computational Study, pp. 1–81. Princeton University Press, Princeton (2006)
Gutin, G., Punnen, A.P.: The Traveling Salesman Problem and Its Variations, pp. 1–28. Springer, New York (2007)
Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations, pp. 85–103. Springer, New York (1972)
Holland, J.H.: Adaption in Natural and Artificial Systems. The MIT Press, Cambridge (1992)
Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans. Fuzzy Syst. 7, 608–616 (1999)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, ICNN 1995, Perth, WA, Australia, vol. 4, pp. 1942–1948 (1995)
Balázs, K., Botzheim, J., Kóczy, T.L.: Comparison of various evolutionary and memetic algorithms. In: Huynh, V.-N., Nakamori, Y., Lawry, J., Inuiguchi, M. (eds.) Integrated Uncertainty Management and Applications, vol. 68, pp. 431–442. Springer, Heidelberg (2010)
Botzheim, J., Cabrita, C., Kóczy, L.T., Ruano, A.E.: Fuzzy rule extraction by bacterial memetic algorithms. In: Proceedings of the 11th World Congress of International Fuzzy Systems Association, IFSA 2005, Beijing, China, pp. 1563–1568 (2005)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts-towards memetic algorithms, Technical Report Caltech Concurrent Computation Program, Report 826, California Institute of Technology, Pasadena, USA (1989)
Dányádi, Zs., Földesi, P., Kóczy, T.L.: A fuzzy bacterial evolutionary solution for crisp three-dimensional bin packing problems. In: IEEE World Congress on Computational Intelligence, Brisbane, Australia, WCCI 2012 (2012)
Farkas, M., Földesi, P., Botzheim, J., Kóczy, T.L.: Approximation of a modified Traveling Salesman Problem using Bacterial Memetic Algorithms. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds.) Towards Intelligent Engineering and Information Technology, SCI 243, pp. 607–625. Springer, Heidelberg (2009)
Applegate, D.L., Bixby, R.E., Chvátal, V., Cook, W.J., Espinoza, D., Goycoolea, M., Helsgaun, K.: Certification of an optimal tour through 85,900 cities. Oper. Res. Lett. 37(1), 11–15 (2009)
Kóczy, L.T., Földesi, P., Tüű-Szabó, B.: A Discrete Bacterial Memetic Evolutionary Algorithm for the Traveling Salesman Problem. In: The Congress on Information Technology, Computational and Experimental Physics (CITCEP 2015), Cracow, Poland, pp. 57–63 (2015)
Tang, M., Yao, X.: A memetic algorithm for VLSI floorplanning. Syst. Man Cybernet. 37(1), 62–69 (2007)
Földesi, P., Botzheim, J.: Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm. Memetic Comput. 2(4), 259–271 (2010)
Hoos, H.H., Stutzle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2005)
Reinelt, G.: TSPLIB – A traveling salesman problem library. ORSA J. Comput. 3, 376–385 (1991)
VLSI TSP dataset, April 2015. http://www.math.uwaterloo.ca/tsp/vlsi/index.html
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Tüű-Szabó, B., Földesi, P., Kóczy, L.T. (2017). Improved Discrete Bacterial Memetic Evolutionary Algorithm for the Traveling Salesman Problem. In: Phon-Amnuaisuk, S., Au, TW., Omar, S. (eds) Computational Intelligence in Information Systems. CIIS 2016. Advances in Intelligent Systems and Computing, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-319-48517-1_3
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