Abstract
This paper proposes a variant of the bacterial foraging optimization (BFO) algorithm with time-varying chemotaxis step length and comprehensive learning strategy, namely Adaptive Comprehensive Learning Bacterial Foraging Optimization (ALCBFO). An adaptive non-linearly decreasing modulation model is used to balance the exploration and exploitation. The comprehensive learning mechanism is adopted to maintain the diversity of the bacterial population and thus alleviates the premature convergence. Compared with the classical GA, PSO, the original BFO and two improved BFOs (BFO-LDC and BFO-NDC), the proposed ACLBFO shows significantly better performance in solving multimodal problems.
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
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 52–67 (2002)
Panigrahi, B.K., Pandi, V.R.: Bacterial foraging optimization: Nelder-Mead hybrid algorithm for economic load dispatch. IET Generation Transmission & Distribution 2(4), 556–565 (2008)
Kou, P.G., Zhou, J.Z., He, Y.Y., Xiang, X.Q., Li, C.S.: Optimal PID governor tuning of hydraulic turbine generators with bacterial foraging particle swarm optimization algorithm. Proceedings of the Chinese Society of Electrical Engineering 29(26), 101–106 (2009)
Niu, B., Fan, Y., Xiao, H., Xue, B.: Bacterial foraging based approaches to portfolio optimization with liquidity risk. Neurocomputing 98(3), 90–100 (2012)
Okaeme, N.A., Zanchetta, P.: Hybrid bacterial foraging optimization strategy for automated experimental control design in electrical drives. IEEE Transactions on Industrial Informatics 9(2), 668–678 (2013)
Niu, B., Wang, H.: Bacterial colony optimization. Discrete Dynamics in Nature and Society, 1–28 (2012)
Niu, B., Fan, Y., Zhao, P., Xue, B., Li, L., Chai, Y.J.: A novel bacterial foraging optimizer with linear decreasing chemotaxis step. In: 2nd International Workshop on Intelligent Systems and Applications (ISA), pp. 1–4 (2010)
Niu, B., Fan, Y., Wang, H.: Novel bacterial foraging optimization with time-varying chemotaxis step. International Journal of Artificial Intelligence, 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)
Liang, J.J., Qin, A.K., Suganthan, P.N.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)
Ashlock, D.: Evolutionary computation for modeling and optimization. Springer, New York (2006)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the IEEE Congress Evolutionary Computation, pp. 1945–1950 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, L., Wang, H., Liang, X., Xing, K. (2013). An Adaptive Comprehensive Learning Bacterial Foraging Optimization for Function Optimization. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-39678-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
Online ISBN: 978-3-642-39678-6
eBook Packages: Computer ScienceComputer Science (R0)