群居蜘蛛优化算法在水位流量关系拟合中的应用

Application of social spider optimization algorithm in relation fitting of water lever and discharge

针对水位流量关系拟合中相关参数难以确定的不足,利用一种新型群体智能仿生算法——群居蜘蛛优化算法(SSO)优化水位流量关系的相关参数,以云南省丽江仁里站和总管田站水位流量关系拟合为例进行实例研究,并与粒子群优化算法(PSO)、最小二乘法(LSM)拟合结果进行对比。结果表明:SSO算法对仁里站和总管田站水位流量关系拟合的平均相对误差绝对值分别为0.57%、0.53%,拟合精度优于PSO、LSM算法。SSO算法具有收敛速度快、全局寻优能力强等特点,利用SSO算法优化水位流量关系可以获得更好的拟合效果。

Aimed at the lack of parameters being difficult to be determined in the stage of relation fitting of water leveral and discharge, the paper used a new kind of swarm intelligent bionic algorithm--communal spiders optimization algorithm （SSO） to optimize the correlation parameters of water level and flow, and took the relation fitting of water lever and discharge in Renli and Zongguantian station of Lijiang river of Yunnan Province for example , and compared the results with that of particle swarm optimization algorithm （PSO） and least square method （LSM） fitting. The results show that the average relative error absolute value of SSO algorithm for the fitting of water level and flow relationship in the two stations is 0.57% and 0.53% respectively, and the fitting precision is better than that of PSO and LSM. SSO algorithm has the advantages of fast convergence speed, strong global optimization ability, and so on. Using SSO algorithm to optimize the relationship water level and flow can get a better fitting effect.