Abstract
A new metaheuristic, bat algorithm, inspired by echolocation characteristics of micro-bats has been extensively applied to solve various continuous optimization problems. Numerous intelligent techniques are hybridized with bat algorithm to optimize its performance. However, there are only two discrete variants have been proposed to tune the basic bat algorithm to handle combinatorial optimization problems. However, both of them suffer from the inherited drawbacks of the bat algorithm such as slow speed convergence and easy stuck at local optimal. Motivated by this, an improved hybrid variant of discrete bat algorithm, called IHDBA is proposed and applied to solve traveling salesman problem. IHDBA achieves a good balance between intensification and diversification by adding the evolutionary operators, crossover and mutation, which allow performance of both local and global search. In addition, 2-opt and 3-opt local search techniques are introduced to improve searching performance and speed up the convergence. Using extensive evaluations based on TSP benchmark instances taken from TSPLIB, the results show that IHDBA outperforms state-of-the-art discrete bat algorithm i.e. IBA in the most of instances with respect to average and best solutions.
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References
Stutzle, T., Hoos, H.: Stochastic Local Search: Foundations and Applications (2005)
Laporte, G.: The traveling salesman problem: an overview of exact and approximate algorithms. Eur. J. Oper. Res. 59(2), 231–247 (1992)
Lenstra, J.K., Kan, A.R.: Some simple applications of the travelling salesman problem. Oper. Res. Q. (1970–1977) 26(4), 717–733 (1975). http://www.jstor.org/stable/3008306
Reinelt, G.: The Traveling Salesman: Computational Solutions for TSP Applications. Springer, Heidelberg (1994)
Yang, X.-S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput. 5(3), 141–149 (2013)
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature inspired cooperative strategies for optimization (NICSO 2010). Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Heidelberg (2010)
Wang, G., Guo, L.: A novel hybrid bat algorithm with harmony search for global numerical optimization. J. Appl. Math. 2013, 21 (2013)
Pan, J-S., Dao, T.-K., Kuo, M.-Y., Horng, M.-F., et al.: Hybrid bat algorithm with artificial bee colony. In: Pan, J.-S., Snasel, V., Corchado, E.S., Abraham, A., Wang, S.-L. (eds.) Intelligent Data analysis and its Applications, Volume II, vol. 298, pp. 45–55. Springer, Heidelberg (2014)
Pan, T.S., Dao, T.-K., Chu, S.-C., et al.: Hybrid particle swarm optimization with bat algorithm. In: Sun, H., Yang, C.-Y., Lin, C.-W., Pan, J.-S., Snasel, V., Abraham, A. (eds.) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol. 329. Springer, Heidelberg (2015)
Meng, X., Gao, X., Liu, Y.: A novel hybrid bat algorithm with differential evolution strategy for constrained optimization. Int. J. Hybrid Inf. Technol. 8(1), 383–396 (2015)
Wang, G., Guo, L., Duan, H., Liu, L., Wang, H.: A bat algorithm with mutation for UCAV path planning. Sci. World J. 2012, 15 (2012)
Zhang, J.W., Wang, G.G.: Image matching using a bat algorithm with mutation. In: Applied Mechanics and Materials, vol. 203, pp. 88–93. Trans Tech Publications (2012)
Fister Jr., I., Fister, D., Yang, X.-S.: A hybrid bat algorithm, ArXiv e-prints, March 2013
Perez, J., Valdez, F., Castillo, O.: Modification of the bat algorithm using fuzzy logic for dynamical parameter adaptation. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 464–471, May 2015
Lin, J.-H., Chou, C.-W., Yang, C.-H., Tsai, H.-L., et al.: A chaotic levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems. Comput. Inf. Technol. 2(2), 56–63 (2012)
Abdel-Raouf, O., Abdel-Baset, M., El-Henawy, I.: An improved chaotic bat algorithm for solving integer programming problems. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 6(8), 18 (2014)
Gandomi, A.H., Yang, X.-S.: Chaotic bat algorithm. J. Comput. Sci. 5(2), 224–232 (2014)
Khan, K., Nikov, A., Sahai, A.: A fuzzy bat clustering method for ergonomic screening of office workplaces. Third International Conference on Software, Services and Semantic Technologies S3T. Advances in Intelligent and Soft Computing, vol. 101, pp. 59–66. Springer, Heidelberg (2011)
Saji, Y., Riffi, M.E., Ahiod, B.: Discrete bat-inspired algorithm for travelling salesman problem. In: Second World Conference on Complex Systems (WCCS), pp. 28–31. IEEE (2014)
Osaba, E., Yang, X.-S., Diaz, F., Lopez-Garcia, P., Carballedo, R.: An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Engineering Applications of Artificial Intelligence 48, 59–71 (2016)
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Al-sorori, W., Mohsen, A., Aljoby ßer, W. (2016). An Improved Hybrid Bat Algorithm for Traveling Salesman Problem. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_47
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DOI: https://doi.org/10.1007/978-981-10-3611-8_47
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