蛙跳算法优化的地下水质评价的参数化组合算子模型

传统的参数化多元组合算子模型在进行对数变换后,采用最小二乘法求解模型参数。在满足构建的优化目标函数条件下,直接用基于群集智能的混合蛙跳算法对参数化组合算子模型中的参数进行优化,避免了传统解法过程中需要进行的对数变换,因而使参数求解变得简化。改进后的参数化组合算子模型被应用于地下水水质评价,其评价结果与用其他多种方法的评价结果基本一致。从而表明:混合蛙跳算法优化得出的地下水水质评价参数化组合算子模型为地下水水质评价提供了一种简便和实用的新方法。

PARAMETRIC COMBINATION OPERATOR MODEL OPTIMIZED BY SHUFFLED FROG LEAPING ALGORITHM FOR GROUNDWATER QUALITY ASSESSMENT

Traditional parameter optimization in the parametric combination operator model was used by the method of least square with the aid of logarithmic transformation.The parameter involved in the parametric combination operator model was optimized by using shuffled frog leaping algorithm(SFLA) based on swarm intelligence,under the satisfying the optimized goal function form.These advantages are free from logarithmic transformation process to model and also simple to solve the parameter.Furthermore,this improved model was applied to the practical groundwater quality assessment.The assessment results are proved to be coincident with that obtained from other evaluation methods.It is sufficiently show that the parametric combination operator model optimized by shuffled frog leaping algorithm is a new simple and practical method for groundwater quality assessment.