Abstract
The bacterial foraging optimization (BFO) algorithm is a new complex, swarm-based optimization algorithm. The algorithm has shown to be successful in static environments; however there is little research available on analysis of its performance in dynamic environments. The aim of this article is to conduct an elaborate empirical analysis of BFO in a number of dynamic environments. Additionally, a modification to BFO is proposed to improve BFO’s performance in dynamic environments.
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.
Similar content being viewed by others
References
Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments 10, 459–472 (2006)
Blackwell, T.M.: Swarms in Dynamic Environments. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003, Part I. LNCS, vol. 2723, pp. 1–12. Springer, Heidelberg (2003)
Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments, pp. 489–500. Springer (2004)
Chatterje, A.: Bacterial foraging techniques for solving EKF-based slam problems. In: Control Conference
Duheim, J.: Particle Swarm Optimization in Dynamically Changing Environment An Empirical Study. Master’s thesis, Department of Computer Science, University of Pretoria (2011)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization, vol. 1, pp. 84–88. IEEE (2000)
Krink, T., Vesterstrom, J.S., Riget, J.: Particle swarm optimisation with spatial particle extension, vol. 2, pp. 1474–1479. IEEE (2002)
Majhi, B., Panda, G.: Recovery of Digital Information Using Bacterial Foraging Optimization Based Nonlinear Channel Equalizers, pp. 367–372 (2007)
Mishra, S., Bhende, C.N., Lai, L.L., Delhi, N., Group, E.S.: Optimization of a distribution static compensator by bacterial foraging technique, pp. 13–16 (August 2006)
Morrison, R.W.: Performance measurement in dynamic environments. Foundations and Trends in Accounting 2(3), 175–240 (2003)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control, vol. 22, pp. 52–67. IEEE (2002)
Ramos, V., Fernandes, C., Rosa, A.C.: On ants, bacteria and dynamic environments (2005)
Tang, W.J., Wu, Q.H., Saunders, J.R.: Bacterial foraging algorithm for dynamic environments, pp. 1324–1330. IEEE (2006)
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
Abbott, J., Engelbrecht, A.P. (2012). Performance of Bacterial Foraging Optimization in Dynamic Environments. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2012. Lecture Notes in Computer Science, vol 7461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32650-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-32650-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32649-3
Online ISBN: 978-3-642-32650-9
eBook Packages: Computer ScienceComputer Science (R0)