混沌粒子群优化算法在马斯京根模型参数优化中的应用

针对目前马斯京根模型参数率定中存在的求解复杂、精度不高等问题,本文将混沌搜索机制引入粒子群优化算法中,构建混沌粒子群优化算法对马斯京根模型参数进行率定。这种方法利用混沌运动的遍历性,改善了粒子群优化算法的全局寻优能力,避免算法陷入局部极值,使得粒子群体的进化速度加快,提高了算法的收敛速度和精度。通过实例应用表明,混沌粒子群优化算法可以有效地估算出马斯京根模型参数,优化效果明显优于粒子群优化算法及试错法,因此该算法具有很好的实用性。

Application of Chaotic Particle Swarm Optimization to Parameter Estimation in Mustingum Model

In order to solve the problem of complexity and poor accuracy for parameter estimation in Muskingum model, the chaos was incorporated into particle swarm optimization, chaotic particle swarm optimization algorithm to estimate the parameter of Mustingum model was set up. This optimization has used the ergodicity of chaos to improve the whole search capability of particle swarm optimization algorithm, avoid embedding local extremum, the evolution process can be quickened and convergence speed and accuracy was improved. The results of application showned that the chaotic particle swarm optimization algorithm can effectively estimate the parameter of Mustingum model, its effect was better than the particle swarm optimization and trial-and-error method, so that the algorithm has better practicability.