改进的萤火虫优化算法求解Van Genuchten方程参数  

Improved Artificial Glowworm Swarm Optimization Algorithm for Solving Parameters of Van Genuchten Equation

作　　者：莫愿斌[1,2] 刘付永[1] 马彦追[1] 

机构地区：[1]广西民族大学,南宁530006 [2]广西混杂计算与集成电路设计分析重点实验室,南宁530006

出　　处：《计算机科学》2013年第11A期131-135,139,共6页Computer Science

基　　金：中国博士后基金(2012M511711);广西教育厅项目(201204LX082);广西民族大学项目(2011MDYB030)资助

摘　　要：Van Genuchten方程是应用最广泛的土壤水分特征曲线方程,该方程的关键是4个参数的取值。为了精确地求解这些参数,引入萤火虫算法进行求解,提出了一种基于生物寄生行为的人工萤火虫优化算法(GSOPB)。该算法将萤火虫群分为寄生群和宿主群两个种群,两种群间隔一定的迭代次数相互交换部分萤火虫;淘汰宿主群中适应度较差的一半萤火虫,以体现"优胜劣汰"的生物进化法则。标准测试函数的仿真结果表明了GSOPB算法的有效性;对Van Genuchten方程参数的优化结果表明,GSOPB算法的求解精度优于其他方法,可以作为求解Van方程参数的新方法。Van Genuchten equation is widely used soil water characteristic curve equation, and its parameter value precision is the key to the use of the equation. In order to solve these parameters accurately, the glowworm swarm optimization（GSO） algorithm was introduced, and a new artificial glowworm swarm optimization algorithm based biological parasitic behavior （GSOPB） was proposed, which consists of the host and the parasite population. The two populations exchange glowworm in a certain number of iterations. In order to embody the rule of survival of the fittest in biological evolution, the glowworm with poor fitness in the host population is removed. The experiment results of some benchmarks show the effectiveness of GSOPB, and the results of solving parameters of Van Genuchten show good performance of GSOPB compared with the other methods. The algorithm can be used as a new method to calculate Van Genuchten equation parameters.

关 键 词：人工萤火虫算法 寄生行为 土壤水分特征曲线 VAN Genuchten方程 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]