利用混沌粒子群算法确定河流水质模型参数

将混沌寻优思想引入到粒子群优化算法中,提出了混沌粒子群算法,这种方法利用混沌运动的随机性、遍历性和规律性等特性对当前粒子群体中的粒子进行混沌寻优。通过这种处理使得粒子群体的进化速度加快,从而改善了粒子群优化算法摆脱局部极值点的能力,提高了算法的收敛速度和精度。并将混沌粒子群算法应用于求解分析瞬时投放示踪剂情况下的一维河流水团示踪试验数据以及确定河流水质参数的函数优化问题,结果表明,混沌粒子群算法的收敛性能明显优于粒子群优化算法。

Application of Chaos Particle Swarm Optimization Algorithm to Determination of Water Quality Parameter of River Steam

This paper incorporates chaotic search into original particle swarm optimizers, and presents a new chaos particle swarm optimization algorithm. Based on the ergodicity, stochastic property and regularity of chaos, individuals are reproducted by chaotic searching on the current individuals. The particle swarm optimization embedded chaotic search quickens the evolution process, and improve the abilities of seeking the global excellent result and convergence speed and accuracy. And the chaos particle swarm optimization algorithms were applied to analysis of 1D tracing test date of river streams with tracters instantaneously injected, and further to optimization of functions to estimate the water quality parameters of river streams. The results show that the proposed algorithms are superior to original particle swarm optimization algorithms.