Abstract
A novel algorithm named Adaptive Step Searching (ASS) is presented in the paper to solve the stochastic point location (SPL) problem. In the conventional method [1] for the SPL problem, the tradeoff between the convergence speed and accuracy is the main issue since the searching step of learning machine (LM) in the method is invariable during the entire searching. In that case, in ASS, LM adapts the step size to different situations during the searching. The convergence speed has been improved significantly with the same accuracy comparing to previous algorithms.
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
Oommen, B.J.: Stochastic Searching on the Line and Its Applications to Parameter Learning in Nonlinear Optimization. IEEE Transaction Systems, Man, and Cybernetics 27(4), 733–739 (1997)
Oommen, B.J., Raghunth, G., Kuipers, B.: Parameter Learning from Stochastic Teachers and Stochastic Compulsive Liars. IEEE Transaction Systems, Man, and Cybernetics 36(4), 820–836 (2006)
Huang, D.H., Jiang, W.: A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments. IEEE Transaction Systems, Man, and Cybernetics 42(5), 1489–1500 (2012)
Yazidi, A., Granmo, O.-C., Oommen, B.J., Goodwin, M.: A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds.) IEA/AIE 2012. LNCS (LNAI), vol. 7345, pp. 774–783. Springer, Heidelberg (2012)
Misra, S., Oommen, B.J.: GPSPA: A New Adaptive Algorithm for Maintaining Shortest Path Routing Trees in Stochastic Networks. International Journal of Communication Systems 17(10), 963–984 (2004)
Atlassis, A.F., Vasilakos, A.V.: The Use of Reinforcement Learning Algorithms in Traffic Control of High Speed Networks. In: Advances in Computational Intelligence and Learning, vol. 18, pp. 353–369 (2002)
Meybodi, M.R., Beigy, H.: New Learning Automata Based Algorithms for Adaptation of Backpropagation Algorithm Parameters. International Journal of Neural Systems 12(1), 45–67 (2002)
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
Tao, T., Ge, H., Cai, G., Li, S. (2013). Adaptive Step Searching for Solving Stochastic Point Location Problem. In: Huang, DS., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds) Intelligent Computing Theories. ICIC 2013. Lecture Notes in Computer Science, vol 7995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-39479-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39478-2
Online ISBN: 978-3-642-39479-9
eBook Packages: Computer ScienceComputer Science (R0)