Abstract
This paper presents a neuro-genetic approach for solving constrained nonlinear optimization problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. On the other hand, artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. The association of a modified Hopfield network with genetic algorithm guarantees the convergence of the system to the equilibrium points, which represent feasible solutions for constrained nonlinear optimization problems. Simulated examples are presented to demonstrate that proposed method provides a significant improvement.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming. Wiley, New York (1993)
Tank, D.W., Hopfield, J.J.: Simple Neural Optimization Networks: An A/D Converter, Signal Decision Network, and a Linear Programming Circuit. IEEE Trans. on Circuits and Systems 33, 533–541 (1986)
Liang, X.B., Wang, J.: A Recurrent Neural Network for Nonlinear Optimization With a Continuously Differentiable Objective Function and Bound Constraints. IEEE Trans. on Neural Networks 11, 1251–1262 (2000)
Reifman, J., Feldman, E.E.: Multilayer Perceptron for Nonlinear Programming. Computers & Operations Research 29, 1237–1250 (2002)
Hopfield, J.J.: Neurons With a Graded Response Have Collective Computational Properties Like Those of Two-State Neurons. Proc. of the National Academy of Science 81, 3088–3092 (1984)
Aiyer, S.V., Niranjan, M., Fallside, F.: A Theoretical Investigation into the Performance of the Hopfield Network. IEEE Trans. on Neural Networks 1, 53–60 (1990)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Luenberger, D.G.: Linear and Nonlinear Programming. Springer, New York (2003)
Vidyasagar, M.: Nonlinear Systems Analysis. Prentice-Hall, Englewood Cliffs (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bertoni, F.C., da Silva, I.N. (2006). Neuro-genetic Approach for Solving Constrained Nonlinear Optimization Problems. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_91
Download citation
DOI: https://doi.org/10.1007/11893295_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)