Abstract
As a novel optimization technique, neural network based optimization has gained much attention and some applications during the past decade. To enhance the performance of Differential Evolution Algorithm (DEA), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, an intelligent Differential Evolution Algorithm (IDEA) is proposed by incorporating neural network based search behaviors into classic DEA. Firstly, DEA operators are used for exploration by updating individuals so as to maintain the diversity of population and speedup the search process. Secondly, a multi-layer feed-forward neural network is employed for local exploitation to avoid being trapped in local optima and improve the convergence of the IDEA. Simulation results and comparisons based on well-known benchmarks and optimal designing of trading-ratio system for water market demonstrate that the IDEA can effectively enhance the searching efficiency and greatly improve the searching quality.
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References
Tank, D.W., Hopfield, J.J.: Simple ’Neural’ Optimization Network: An A/D Converter, Signal Decision Circuit and a Linear Programming Circuit. IEEE Trans. Circuits and System 33, 533–541 (1986)
Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces. J. Global Optim. 11, 341–359 (1997)
Wang, L.: Intelligent Optimization Algorithms with Applications, 4th edn. Springer, Beijing (2001)
Liu, B., Wang, L., Jin, Y.H., Huang, D.X.: Advances in Particle Swarm Optimization Algorithm. Control and Instruments in Chemical Industry 32, 1–6 (2005)
Price, K., Storn, R.: Differential Evolution Homepage. The URL of which is: http://www.ICSI.Berkeley.edu/~storn/code.html
Hung, M.F., Shaw, D.: A Trading-Ratio System for Trading Water Pollution Discharge Permits. Journal of Environmental Economics and Management (in press)
Shaw, D., Liu, Y., Hong, M.F.: A Trading-ratio System for Water Market. Economic Research 39, 69–77 (2004)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
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© 2007 Springer Berlin Heidelberg
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Liu, Y., Liu, B., Huang, J., Wu, Y., Wang, L., Jin, Y. (2007). An Intelligent Differential Evolution Algorithm for Designing Trading-Ratio System of Water Market. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_129
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DOI: https://doi.org/10.1007/978-3-540-72395-0_129
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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