Abstract
A new hybrid ant colony algorithm was proposed. Firstly, weight factor was introduced to the binary ant colony algorithm, and then we obtained a new probability by combining probability model of Population based incremental learning (PBIL) with transfer probability of ants pheromone . The new population are produced by probability model of PBIL, transfer probability of ants pheromone and the probability of proposed algorithm so that population polymorphism is ensured and the optimal convergence rate and the ability of breaking away from the local minima are improved. Optimization simulation results based on the benchmark test functions show that the hybrid algorithm has higher convergence rate and stability than binary ant colony algorithm (BACA) and Population based incremental learning (PBIL).
This work is supported by NSF of Hebei Province #F2008001166 to Liubo.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford (1999)
Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical Report 91-016, Dipartimento Di Elettronicaq – Politecnico Di Milano (1991)
Dorigo, M., Stützle, T.: Ant colony optimization. The MIT Press, Cambridge (2004)
Xiong, W., Wang, L., Yan, C.: Binary Ant Colony Evolutionary Algorithm. International Journal of Information Technology 12(3) (2006)
Petrovski, A., Shakya, S., McCall, J.: Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, Washington, USA, pp. 413–418. ACM Press, New York (2006)
Yuan, B., Gallagher, M.: Playing in Continuous Spaces: Some Analysis and Extension of Population-Based Incremental Learning. In: Proceedings of Congress of Evolutionary Computation (CEC), pp. 443–450 (2003)
Shapiro, J.L.: The Sensitivity of PBIL to the Learning Rate, and How Detailed Balance Can Remove It. In: Cotta, C., de Jong, K., Poli, R., Rowe, J. (eds.) Foundations of Genetic Algorithms VII. Morgan Kaufmann, San Francisco (2002)
Očenášek, J.: Parallel Estimation of Distribution Algorithms, Brno University of Technology, Faculty of Information Technology, Doctoral Thesis (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, B., Li, H., Wu, T., Zhang, Q. (2008). Hybrid Ant Colony Algorithm and Its Application on Function Optimization. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_84
Download citation
DOI: https://doi.org/10.1007/978-3-540-92137-0_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92136-3
Online ISBN: 978-3-540-92137-0
eBook Packages: Computer ScienceComputer Science (R0)