基于仿生原理的粒子群算法求解江安校区水动力弥散系数

水动力弥散系数是研究地下水溶质运移的一个重要参数。为了解污染物在地下水中的运移规律，利用基于仿生学原理的粒子群算法，求解四川大学江安校区弥散试验场中的潜水含水层天然流场下的水动力弥散系数，并与最小二乘法和标准曲线对比法的计算结果相比较。研究结果表明，标准曲线法的计算结果受人为主观影响误差较大；最小二乘法计算结果与实测数据拟合较好，但计算过程相对复杂；粒子群算法的求解精度最高，计算更快，具有良好的收敛性，是一种可靠的求解方法。 

Particle swarm optimization-based bionic principle for solving hydrodynamic dispersion coefficient in Jiang'an Campus

Hydrodynamic dispersion coefficient is an important parameter in the study of solute transport in groundwater. In order to understand the transport law of pollutants in groundwater, the particle swarm optimization(PSO) algorithm based on bionic principle is used to solve the hydrodynamic dispersion coefficient of phreatic aquifer under natural flow field in the dispersion test site of Jiang'an Campus of Sichuan University. Compared with the least-square method and the standard curve comparison method, the results show that the results of the standard curve method are subject to subjective influence, and the errors are relatively large. The results of least square method fit well with the measured data, but the calculation process is relatively complex. Particle swarm optimization(PSO) is a reliable solution method, has the highest accuracy, faster calculation, good convergence and stability.