摘要
在对基本果蝇优化算法的寻优流程进行深入分析的基础上,提出一种单方向搜索处理的改进果蝇优化算法(IFOA)。该方法可以对极值点为非零非负的非线性函数进行优化处理,将其应用于非线性模型参数估计。实例表明,IFOA方法在参数估计精度上优于线性近似法与非线性迭代方法;与以遗传算法为代表的智能搜索方法相比,其估计精度相当,并具有参数设置少、寻优过程简单、易于程序实现等优点。
Based on deep analysis of the optimization process of the basic fruit fly optimization algorithm,this paper supports an improved fruit fly optimization algorithm（IFOA）for search processing of a single direction.The IFOA method can process the nonlinear function that has nonzero and nonnegative extreme points.Based on this advantage,IFOA method is applied to parameter estimation of a nonlinear model.Analysis results of a practical example show that estimation accuracy of the IFOA method is superior to the linear approximation method and the nonlinear iterative method.Compared with intelligent search methods represented by agenetic algorithm,estimation accuracy is nearly equal.In addition,the IFOA method has several obvious advantages,including fewer parameter settings,ease of finding the best one,and easy programming.
出处
《大地测量与地球动力学》
CSCD
北大核心
2016年第12期1092-1095,共4页
Journal of Geodesy and Geodynamics
基金
国家自然科学基金(41404008)
广西空间信息与测绘重点实验室开放基金(桂科能1103108-21)
江西省数字国土重点实验室开放基金(DLLJ201408)
精密工程与工业测量国家测绘地理信息局重点实验室开放基金(PF2015-12)
福州大学科技发展基金(2014-XQ-33)~~
关键词
果蝇优化算法
单方向搜索处理
非线性模型
参数估计
智能搜索方法
fruit fly optimization algorithm
search processing for single direction
nonlinear model
parameter estimation
intelligent search method
