Abstract
In this paper structure-redesign-based Bacterial Foraging Optimization (SRBFO) is proposed to solve portfolio selection problem. Taking advantage of single-loop structure, a new execution structure is developed in SRBFO to improve the convergence rate as well as lower computational complexity. In addition, the operations of reproduction and elimination-dispersal are redesigned to further simplify the original BFO algorithm structure. The proposed SRBFO is applied to solve portfolio selection problems with transaction fee and no short sales. Four cases with different risk aversion factors are considered in the experimental study. The optimal portfolio selection obtained by SRBFO is compared with PSOs, which demonstrated that the validity and efficiency of our proposed SRBFO in selecting optimal portfolios.
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
Tang, W.J., Li, M.S., Wu, Q.H., Saunders, J.R.: Bacterial Foraging Algorithm for Optimal Power Flow in Dynamic Environments. IEEE Transactions on Circuits and Systems I: Regular Papers 55(8), 2433–2442 (2008)
Ulagammai, M., Venkatesh, P., Kannan, P.S., Prasad Padhy, N.: Application of Bacterial Foraging Technique Trained Artificial and Wavelet Neural Networks in Load Forecasting. Neurocomputing 70(16), 2659–2667 (2007)
Sathya, P.D., Kayalvizhi, R.: Image Segmentation Using Minimum Cross Entropy and Bacterial Foraging Optimization Algorithm. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 500–506. IEEE Press (2011)
Niu, B., Fan, Y., Wang, H., Wang, X.: Novel Bacterial Foraging Optimization with Time-Varying Chemotaxis Step. International Journal of Artificial Intelligence 7(A11), 257–273 (2011)
Azizipanah-Abarghooee, R.: A New Hybrid Bacterial Foraging and Simplified Swarm Optimization Algorithm for Practical Optimal Dynamic Load Dispatch. International Journal of Electrical Power & Energy Systems 49, 414–429 (2013)
Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control. IEEE Control Systems Magazine, 52–67 (2002)
Liu, Y., Passino, K.M.: Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors. Journal of Optimization Theory and Applications 115(3), 603–628 (2002)
Li, L., Xue, B., Tan, L., Niu, B.: Improved Particle Swarm Optimizers with Application on Constrained Portfolio Selection. In: Huang, D.-S., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2010. LNCS, vol. 6215, pp. 579–586. Springer, Heidelberg (2010)
Niu, B., Fan, Y., Xiao, H., Xue, B.: Bacterial Foraging-Based Approaches to Portfolio Optimization with Liquidity Risk. Neurocomputing 98(3), 90–100 (2012)
Niu, B., Wang, H., Chai, Y.J.: Bacterial Colony Optimization. Discrete Dynamics in Nature and Society 2012, 28 (2012)
Niu, B., Wang, H., Wang, J.W., Tan, L.J.: Multi-objective Bacterial Foraging Optimization. Neurocomputing 116, 336–345 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Niu, B., Bi, Y., Xie, T. (2014). Structure-Redesign-Based Bacterial Foraging Optimization for Portfolio Selection. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-09330-7_49
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09329-1
Online ISBN: 978-3-319-09330-7
eBook Packages: Computer ScienceComputer Science (R0)