Abstract
In this paper we present three ant colony optimization (ACO\(_R\)) with different crossover operations to solve the continuous optimization problems. Crossover operations in the genetic algorithm are employed to generate some new probability density function set (PDFs) of ACO\(_R\) in the promising space, which is aimed at improving the global exploration ability of ACO\(_R\), and avoiding falling into the local minima and exploiting the correlation information among the design variables. The proposed algorithm is evaluated on some benchmark functions and the simulation results show that the proposed algorithm performs quite well and outperforms other algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, X., Tian, Y., Jin, Y.: A knee point driven evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. (2014). doi:10.1109/TEVC.2014.2378512
Zhang, X., Tian, Y., Cheng, R., Jin, Y.: An efficient approach to non-dominated sorting for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 19(2), 201–213 (2015)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)
Hu, X., Zhang, J., Li, Y.: Orthogonal methods based ant colony search for solving continuous optimization problems. J. Comput. Sci. Technol. 23, 2–18 (2008)
Liao, T., Stutzle, T.: A unified ant colony optimization algorithm for continuous optimization. Eur. J. Oper. Res. 234, 597–609 (2014)
Eshelman, L., Schaffer, J.: Real-coded genetic algorithms and interval schemata. In: Whitley, D.L. (ed.) Foundation of Genetic Algorithms II, pp. 187–202. Morgan Kaufmann, San Mateo (1993)
Ono, I., Kobayashi, S.: A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover. In: Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 246–253. Morgan Kaufmann, San Mateo (1997)
Ballester, P.J., Carter, J.N.: An effective real-parameter genetic algorithm with parent centric normal crossover for multimodal optimisation. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 901–913. Springer, Heidelberg (2004)
Chen, Z., Wang, R.: A new framework with fdpp-lx crossover for real-coded genetic algorithm. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E94.A(6), 1417–1425 (2011)
Price, K., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Acknowledgments
This work was supported by Natural Science Foundation Project of CQ CSTC (No. cstc2012jjA40041, No. cstc201-jjA40059) and Science Research Fund of Chongqing Technology and Business University (No. 1153005).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Z., Jiang, Y., Wang, R. (2015). Ant Colony Optimization with Different Crossover Schemes for Continuous Optimization. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-662-49014-3_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49013-6
Online ISBN: 978-3-662-49014-3
eBook Packages: Computer ScienceComputer Science (R0)