基于改进果蝇算法的非线性模型参数估计方法

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。

Parameter Estimation Method for Nonlinear Model Based on Improved Fruit Fly Optimization Algorithm

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 a genetic 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.