An Improved Genetic Algorithm And Its Application In The Rainfall-runoff Model

The thesis makes systemic analysis of the fundamental theory of the genetic algorithms, points out the shortage of the standard genetic algorithm and puts forward an improved genetic algorithm. The calibration of the many models in water resource is very complex. It is high dimensions and nonlinear. It is convex and not continuous. It has many local optima. Considering the excellence of the genetic algorithms, the thesis proposes to use the improved genetic algorithm to solve the problems of the model calibration of the water resource. In the calibration of the rainfall-runoff model, the thesis applies the improved genetic algorithm successfully and improves the efficiency and the precision. The main achievements are as follows:1. The thesis gives the description of the general model of the binary genetic algorithms and the standard genetic algorithm, and introduces the fundamental theory of the genetic algorithms.2. Based on the contradiction of the requirements of the Schema Theory and the convergent speed and the precision of the results of the genetic algorithms, the thesis proposes an improved genetic algorithm. Furthermore, the thesis analyzes its convergence in theory and proves its advantage in precision (compares with the standard genetic algorithm) by using the tool of typical test function.3. Applying the improved genetic algorithm to the calibration of the three-source XinAnJiang model, the thesis gets better results in which shows the advantage of the improved genetic algorithm. In the process of the selection of the fitness function, the thesis does lots of theoretical analysis and experiments, finally combines the objective function and the fitness function together appropriately.4. According to the shortage of traditional single-objective optimization, the thesis proposes to use amendatory objective function parameters optimization. By the comparison of the results of the Misai Valley it shows that the amendatory objective function parameters optimization is better. Because it pays more attention to the balance relationship of the essential factors and can fulfill the different requirements of the social activities.