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
作者
王建伟
彭亦功
WANG Jian-wei;PENG Yi-gong(School of Information Science and Engineering,East China University Science and Technology,Shanghai 200237,Chin)
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2018年第4期617-624,共8页
Journal of East China University of Science and Technology
关键词
鸟群算法
迁移策略
变异策略
参数估计
bird swarm algorithm
migration strategy
mutation strategy
parameter estimation
