文献类型：期刊文献

中文题名：引入迁移和变异策略的改进鸟群算法及其在参数估计中的应用

英文题名：Improved Bird Swarm Algorithm Based on Migration and Mutation Strategy and Its Application in Parameter Estimation

作者：王建伟[1];彭亦功[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2018

卷号：44

期号：4

起止页码：617

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2017_2018】；

语种：中文

中文关键词：鸟群算法;迁移策略;变异策略;参数估计

外文关键词：bird swarm algorithm;migration strategy;mutation strategy;parameter estimation

摘要：针对鸟群算法(BSA)易陷入局部最优的问题,提出了一种引入迁移策略和变异策略的改进鸟群算法(IBSA)。在鸟群飞行阶段引入迁移策略有助于提高鸟群向适应度更高位置迁移的能力,提高BSA的收敛速度;在寻优后期引入变异策略,提高鸟群的局部寻优能力,提高了算法的寻优能力。选取6个典型的测试函数进行寻优实验,实验结果表明,与粒子群算法(PSO)、蝙蝠算法(BA)、BSA等算法相比,IBSA具有更高的寻优精度和更快的寻优速度。在此基础上,将IBSA应用于发酵动力学模型参数估计中,与Gauss-Newton、GA、MAEA算法相比,IBSA的参数估计值的偏差平方和最小,具有更高的模型拟合精度。在面对非凸、不可微等复杂寻优问题的情况下,IBSA为研究者提供了一种更加可靠、快速和精确的寻优可能。
In order to deal with the shortcoming of the local optima of the bird swarm algorithm （BSA）, an improved bird swarm algorithm （IBSA） is proposed in this paper by introducing the migration strategy and the mutation strategy. In the stage of flight, the migration strategy is adopted to raise the ability of bird swarm migration and the convergence speed of BSA. In the later stage of convergence of the BSA, the mutation strategy is utilized to optimize the local searching of the bird swarm and improve the searching ability of the proposed algorithm. Six typical test functions are selected to perform the optimization experiments which are implemented by particle swarm optimization （PSO）, bat algorithm （BA）, BSA, and IBSA, respectively. It is shown from the above experimental results that IBSA has the highest convergence precision and the fastest searching speed. Finally, IBSA is used to estimate the parameters of the fermentation kinetic models. Compared with Gauss Newton, GA and MAEA, IBSA can obtain the smallest value of square sum of deviations squares. Hence, IBSA has the highest model fitting precision and the highest model fitting reliable, fast and accurate optimization the non differentiable. the four algorithms, which also plex optimization problems such means that IBSA is a non convex and