Economic Optimization of River Management Using Genetic Algorithms

In this research, we investigated the potential of a genetic algorithm based technique to optimize the operation of a complex water resources problem. Current approaches to this problem represent a tradeoff between model accuracy and optimization capability. Both a dynamic programming and genetic algorithm approach were applied to a simple water resources exercise. As the exercise grew in complexity, the calculation time for the dynamic programming approach increased rapidly. The genetic algorithm approach experienced a much smaller increase in calculation time. The genetic algorithm approach was then applied to the problem of optimizing the operation a complex simulation model of the Rio Grande Project (RGP) in southern New Mexico. Although it did not model the behavior of the RGP with complete accuracy, the simulation model was representative of the complexity required to do so. The genetic algorithm was able to guide the search to better operating strategies, demonstrating the potential of genetic algorithms to optimize the operation of realistic system models when they are available.