Abstract
In this paper we proposes a new optimization algorithm—Bacterial Colony Optimization (BCO) which formulates the bacterial behavior model in a new way. The model is based on the principle of artificial bacterial behavior, including Chemotaxis, Communication, Elimination, Reproduction and Migration. The Chemotaxis and Communication are spread over the whole optimization process while other behaviors are implemented only when their relevant conditions are reached. Experiment results have proved a high efficiency searching capability of the new proposed artificial bacterial colony.
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
Preview
Unable to display preview. Download preview PDF.
References
Tan, Q., He, Q., Zhao, W.Z.: An Improved FCMBP Fuzzy Clustering Method Based on Evolutionary Programming. Computers & Mathematics with Applications 6(4), 1129–1144 (2010)
Vasconcelos, J.A., Ramirez, J.A., Takahashi, R.H.C., Saldanha, R.R.: Improvements in Genetic Algorithms. IEEE Transactions on Magnetics 37(5), 3414–3417 (2001)
Akbari, R., Ziarati, K.: A Multilevel Evolutionary Algorithm for Optimizing Numerical Functions. International Journal of Industrial Engineering Computations 2, 419–430 (2011)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of International Conference on Neural Networks, vol. 4(3), pp. 1942–1948 (1995)
Karaboga, D., Akay, B.: A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation 214, 108–132 (2009)
Dorigo, M., Birattari, M., Stutzle, T.: Ant Colony Optimization. Computational Intelligence Magazine 1(4), 28–39 (2006)
Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control. IEEE Control Systems Magazine 22(3), 52–67 (2002)
Muller, S.D., Marchetto, J., Airaghi, S., Koumoutsakos, P.: Optimization Based on Bacterial Chemotaxis. IEEE Transactions on Evolutionary Computation 6(1), 16–30 (2002)
Chu, Y., Mi, H., Liao, H.L., Zhen, J., Wu, Q.H.: A Fast Bacterial Swarming Algorithm for High-Dimensional Function Optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 3135–3140 (2008)
Niu, B., Fan, Y., Wang, H.: Novel Bacterial Foraging Optimization with Time-varying Chemotaxis Step. International Joural of Artifical Intelligence 7, 257–273 (2011)
Niu, B., Wang, H., Tan, L.J., Li, L.: Improved BFO with Adaptive Chemotaxis Step for Global Optimization. In: International Conference on Computational Intelligence and Security (CIS), pp. 76–80 (2011)
Niu, B., Wang, H., Tan, L.J., Xu, J.: Multi-Objective Optimization Using BFO Algorithm. In: Huang, D.-S., Gan, Y., Premaratne, P., Han, K. (eds.) ICIC 2011. LNCS, vol. 6840, pp. 582–587. Springer, Heidelberg (2012)
Niu, B., Xue, B., Li, L., Chai, Y.: Symbiotic Multi-swarm PSO for Portfolio Optimization. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5755, pp. 776–784. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Niu, B., Wang, H. (2012). Bacterial Colony Optimization: Principles and Foundations. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_73
Download citation
DOI: https://doi.org/10.1007/978-3-642-31837-5_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31836-8
Online ISBN: 978-3-642-31837-5
eBook Packages: Computer ScienceComputer Science (R0)