非线性马斯京根模型参数优化的混沌模拟退火法

Application of Chaotic Simulated Annealing Algorithm to Parameter Optimization of Nonlinear Muskingum Model

提出了用混沌模拟退火法估计非线性马斯京根模型参数的优化算法。该算法将混沌优化的粗搜索、细搜索的两阶段搜索规则与模拟退火的启发式规则相结合进行参数的优化估计,既保证了解的全局最优性,又保证了最优解的精度。通过实例计算,并与文献方法计算的结果对比分析,表明该方法具有适应性强、求解精度高的特点。

This paper presents an optimization algorithm based on chaotic simulated annealing algorithm, which is used to estimate the parameter of Nonlinear Muskingum model. The algorithm, combining large-scope search and fine search, two stage of chaotic optimization, with heuristic rule of simulated annealing, is used to estimate optimally the model parameter, which ensures the global optimization of the solution and the precision of optimum solution. Through calculation examples, and compared with the results of a document, it shows that this method is characterized by good usability and high precision.