改进细菌觅食算法在PID参数整定中的应用  

Application of improved bacteria foraging algorithm in PID parameter setting

作　　者：李晓含 王联国[2] LI Xiao-han;WANG Lian-guo(School of Mechanical and Electrical Engineering,Gansu Agricultural University,Lanzhou 730070,China;School of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China)

机构地区：[1]甘肃农业大学机电工程学院,甘肃兰州730070 [2]甘肃农业大学信息科学技术学院,甘肃兰州730070

出　　处：《传感器与微系统》2018年第8期157-160,共4页Transducer and Microsystem Technologies

基　　金：甘肃省教育信息化发展战略研究项目(2011-02)

摘　　要：针对细菌觅食算法存在优化精度低、收敛速度较慢、易陷入局部最优等缺点,提出了一种改进的细菌觅食优化算法。将改进粒子群算法运用到细菌觅食算法趋化中,改变步长的大小与翻转方向,加快个体间的信息交流,以提升细菌觅食算法的收敛速度及精度;利用适应度值大小选择迁徙方式,迁移方式有直接迁徙、概率迁徙、不迁徙,个体迁徙概率依据适应度值动态调整,减少误迁或停止不前情况。利用6个标准函数与比例—积分—微分(PID)参数整定进行仿真实验,并对细菌觅食算法和改进细菌觅食算法结果进行比较,验证改进算法的有效性及实用性。Aiming at the shortcomings such as low optimization precision, low convergence rate and easy to fall into local optimum for bacteria foraging algorithm, an improved optimization algorithm of bacteria foraging is proposed. The improved particle swarm algorithm is applied to ehemotaxis of bacteria foraging algorithm to change size of step and the direction of the flip, to speed up the exchange of information between individuals to enhance the convergence rate and precision of the foraging algorithm. Use the fitness value to choose the migration mode, the migration mode includes direct migration, probabilistic migration and not migratory, and the probability of individual migration is dynamically adjusted according to the fitness value to reduce the misplaced or stopped situation. The simulation experiments are carried out with six standard functions and proportion integration differentiation（ PID ） parameters. Compare the results of the algorithm of bacterial foraging and the improved bacterial foraging algorithm to verify the effectiveness and practicability of the improved algorithm.

关 键 词：细菌觅食优化算法 粒子群优化算法 自适应概率 测试函数 比例-积分-微分参数整定 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]