Improved Self-Adaptive Chaotic Genetic Algorithm for Hydrogeneration Scheduling
Publication: Journal of Water Resources Planning and Management
Volume 134, Issue 4
Abstract
The short-term optimal hydrogeneration planning is a complicated nonlinear constrained optimization problem with water delay time. To overcome the shortcomings of a standard genetic algorithm, this paper proposes a new real-value encoding self-adaptive chaotic genetic algorithm to solve this problem, which designs a new crossover operator in light of probability distribution function and a self-adaptive chaotic mutation operator combined chaotic dynamic character with artificial neural network theory. Constraints can be dealt with by using a simple direct comparison penalty function method without the need of any penalty coefficient. The feasibility of the proposed method is demonstrated for short-term generation scheduling of two test hydrosystems and the test results are compared with those obtained by the standard genetic algorithm in terms of solution quality and convergence characteristic. The simulation results show that the proposed method is capable of obtaining higher quality solutions.
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Acknowledgments
The writers gratefully acknowledge financial support from the National Natural Science Foundation of China under Grant Nos. NNSFC50779020, NNSFC40572166, and NNSFC50539140.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 134 • Issue 4 • July 2008
Pages: 319 - 325
Copyright
© 2008 ASCE.
History
Received: May 10, 2006
Accepted: Nov 7, 2007
Published online: Jul 1, 2008
Published in print: Jul 2008
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