Research Article Open Access

Genetic Algorithms Connected Simulation with Smoothing Function for Searching Rule Curves

Anongrit Kangrang and Chavalit Chaleeraktrakoon

Abstract

Rule curves are fundamental guidelines for operating a reservoir system. The objective of this paper is to find a suitable objective function and to propose a smoothing function constraint for searching the optimal rule curves by using genetic algorithms connected simulation model. The results show that an average water shortage is the optimal objective function for searching the optimal rule curves. It can represent the situations of water deficit and excess release. The results also indicate that a moving average applied to be the constraint of searching can reduce the variation of the upper and lower rule curves. Further, the developed model has been applied to determine the optimal rule curves of the Bhumibol and Sirikit Reservoirs (the Chao Phraya River Basin, Thailand). It is shown that the model gives the rule curves which are more mitigate the situations of water deficit and excess release than the existing rule curves. It is also concluded that the genetic algorithms connected simulation with the smoothing constraint is more effective than the model without constraint.

American Journal of Applied Sciences
Volume 4 No. 2, 2007, 73-79

DOI: https://doi.org/10.3844/ajassp.2007.73.79

Submitted On: 29 November 2006 Published On: 28 February 2007

How to Cite: Kangrang, A. & Chaleeraktrakoon, C. (2007). Genetic Algorithms Connected Simulation with Smoothing Function for Searching Rule Curves. American Journal of Applied Sciences, 4(2), 73-79. https://doi.org/10.3844/ajassp.2007.73.79

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Keywords

  • Genetic algorithm
  • Simulation model
  • Smoothing function
  • Rule curves