Study On Parameter Optimization Algorithm And Its Application In Hydrological Model

The usual optimization methods of simplex' s algorithm, Rosen Brock algorithm, Hooke-Jeeveshybrid algorithm, the reformation optimization methods of simulated annealing algorithm, chaos algorithm, maximum entropy method and the potential genetic algorithm are systematically analyzed. Improved accelerating genetic algorithm and hybrid accelerating genetic algorithm are proposed for lots of multidimension, multiapex, nonlinear, discontinuity, nonprotruding about complex parameter optimization problem in Hydrological model. Comparisons with some usual optimization methods were made through the application of Hydrological model in practical. Results showed that the above improved accelerating genetic algorithm have the features of convenient, fast, constringency and steady. They could be applied extensively in the optimal problems. The main achievements are as follows:1. Crossover operator and mutation operator are modified for binary accelerating genetic algorithm. Improved accelerating genetic algorithm is presented. The total optimization ability is improved.2. An chaos algorithm is established by use of the intrinsic stochastic property and ergodicity of chaos movement.3. According to population' s fact, crossover operator and mutation operator are adjusted for accelerating genetic algorithm in evolution in time. Adaptive accelerating genetic algorithm for dynamic crossover and mutation operator are presented. Early constringency is conquered. The total optimization ability is improved.4. The structure in problem can be difficultly reflected by use of binary code. GA local searching ability is quite worse for continuum function. Near binary code integers could be of large Hamming distance, that will debase genetic algorithm' s searching efficiency. Although gray code genetic algorithm can get over the Hamming distance problem, the convergence speed of gray code genetic algorithm is also slow. So gray code accelerating genetic algorithm is presented for continuum function. For the parameter identification of determining the transverse difussion coefficient of river, the optimum design of structures and minimax problems, gray code accelerating genetic algorithm gets good solution. Searching efficiency and solution' s precision are greatly improved for gray code genetic algorithm.5. Binary coding need frequent coding and decoding, and the amount ofcalculation is big. Al though real coding genetic algorithm needn' t frequent coding and decoding, local searching ability of real coding genetic algorithm is sometime difference. Simplex' s algorithm, simulated annealing algorithm or Hooke-Jeeves algorithm is added in real coding genetic algorithm, Simplex hybrid accelerating genetic algorithm, simulated annealing hybrid accelerating genetic algorithm and Hooke-Jeeves hybrid accelerating genetic algorithm are presented, at a certain extent the calculation steps of algorithm is reduced, searching efficiency, global optimization ability and solution' s precision are improved.6. Two-point crossover and two-point mutation' s scheme theorem is established for above gray code accelerating genetic algorithm. It provides a theory base for above gray code accelerating genetic algorithm.7. Simulated annealing algorithm is improved, an improved simulated annealing algorithm is established and searching efficiency is improved for simulated annealing algorithm.8. DFP and RAGA on maximum entropy theory are proposed, an environment optimization problem is good solved.9 . The global convergence is numerically tested and analized for ten optimization methods. Result indicating Hybrid accelerating genetic algorithms---SAGA, JI1GA are good for the global convergence.10. Calculation result of twelve parameter optimization methods is given for Hydrological model. Hybrid accelerating genetic algorithm are established for Hydrological model especially for basin Hydrological model.