混沌粒子群优化算法在确定含水层参数中的应用

以泰斯公式为例,将混沌粒子群优化算法应用于求解分析抽水试验数据,解决含水层参数的函数优化问题.通过在粒子群算法的初始化粒子位置及后续的细搜索过程中加入混沌序列,提高了算法的收敛速度和精度.数值实验结果表明:混沌粒子群算法能够有效地应用于求解含水层参数计算问题；粒子数的增多对混沌粒子群算法收敛性的影响不明显；待估导水系数选取不同的倍数均体现出混沌粒子群算法的收敛性明显优于粒子群优化算法.混沌粒子群算法应用于确定含水层参数是可行的.

Application of chaos particle swarm optimization algorithms to estimation of aquifer parameters

Taking taylor's formula for example, the paper applied chaos partiele swarm optimization algorithm to analyze pumping test daga so as to slove the function optimization problem of parameters in aquifer. Through particle swarm optimization algorithm in the initial position and fine search process the chaotic sequence was added. The numerical experiment results show that the CPSO algorithms are effective to solve the aquifer parameter function optimization problem; the influence of particle increase on the convergence of chaos particle swarm optimization algorithms are not obvious; the initial value of conductivity selecting different multiples embodied that chaos particle swarm optimization algorithm is superior to the convergence of particle swarm optimization algorithms. To apply the CPSO algorithms to determining the aquifer parameters is feasible.