Brownian Distribution Guided Bacterial Foraging Algorithm for Controller Design Problem
Bacterial Foraging Optimization (BFO) algorithm is widely adopted to solve a variety of engineering optimization tasks. In this paper, the Brownian Distribution (BD) strategy guided BFO algorithm is proposed. During the optimization exploration, BD monitors and controls the chemotaxis operation of the BFO algorithm inorder to enhance the search speed and optimization accuracy. In the proposed algorithm, after undergoing a chemotaxis step, each bacterium gets mutated by a BD operator. In the proposed work, this algorithm is employed to design the PID controller for an AVR system and unstable reactor models. The success of the proposed method has been confirmed through a comparative analysis with PSO, BFO, adaptive BFO and PSO + BFO based hybrid methods existing in the literature. The result shows that, for unstable reactor models, the BD guided BFO algorithm provides better optimization accuracy compared to other algorithms considered in this study.
KeywordsBacterial Foraging Algorithm Brownian Distribution PID controller design AVR system unstable reactor
Unable to display preview. Download preview PDF.
- 1.Liu, G.P., Yang, J.-B., Whidborne, J.F.: Multiobjective Optimization and Control. Prentice Hall, New Delhi (2008)Google Scholar
- 2.Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)Google Scholar
- 4.Chen, H., Zhu, Y., Hu, K.: Cooperative Bacterial Foraging Optimization. Discrete Dynamics in Nature and Society 2009, Article ID 815247, 17 pages (2009), doi:10.1155/2009/815247Google Scholar
- 5.Rajinikanth, V., Latha, K.: Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm. Applied Computational Intelligence and Soft Computing 2012, Article ID 214264, 12 pages (2012), doi:10.1155/2012/214264Google Scholar
- 6.Pandi, V.R., Biswas, A., Dasgupta, S., Panigrahi, B.K.: A hybrid bacterial foraging and differential evolution algorithm for congestion management. Euro. Trans. Electr. Power 20(7), 862–871 (2010), doi:10.1002/etep.368Google Scholar
- 9.Korani, W.M., Dorrah, H.T., Emara, H.M.: Bacterial foraging oriented by particle swarm optimization strategy for PID tuning. In: Proceedings of the 8th IEEE International Conference on Computational Intelligence in Robotics and Automation, pp. 445–450 (2008)Google Scholar
- 10.Anguluri, R., Abraham, A., Snasel, V.: A Hybrid Bacterial Foraging - PSO Algorithm Based Tuning of Optimal FOPI Speed Controller. Acta Montanistica Slovaca 16(1), 55–65 (2011)Google Scholar
- 11.Das, S., Biswas, A., Dasgupta, S., Abraham, A.: Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications. In: Abraham, A., Hassanien, A.-E., Siarry, P., Engelbrecht, A. (eds.) Foundations of Computational Intelligence Volume 3. SCI, vol. 203, pp. 23–55. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 12.Rajinikanth, V., Latha, K.: Bacterial Foraging Optimization Algorithm based PID controller tuning for Time Delayed Unstable System. The Mediterranean Journal of Measurement and Control 7(1), 197–203 (2011)Google Scholar
- 14.Nurzaman, S.G., Matsumoto, Y., Nakamura, Y., Shirai, K., Koizumi, S.: From Lévy to Brownian: A Computational Model Based on Biological Fluctuation. PLoS ONE 6(2), e16168 (2011), doi:10.1371/journal.pone.0016168Google Scholar