摘要
针对微粒群优化用于PID参数整定时易陷入局部收敛、效率不高的缺点,提出一种基于动态邻域和自适应惯性权重的微粒群优化算法。首先,通过定义动态邻域及其最优维值,提出种群个体的动态邻域最优维值学习策略,使微粒跟踪个体极值和邻域的最优维值进行搜索,避免局部收敛;其次,提出一种基于个体适应度的惯性权重动态调整方法,提高算法的寻优效率。优化典型测试函数验证了本文所提方法的有效性。最后,将该方法应用于典型工业过程控制的PID参数整定,获得了满意的控制效果。
Aimed at the disadvantage that the particle swarm optimization is easy to fall into the local convergence, and has low efficiency, a particle swarm optimization based on dynamic neighborhood topology and self-adaptive inertia weight is proposed in this paper. Firstly, by defining the dynamic neighborhood and its optimal dimension value, a learning strategy on optimal dimension values of dynamic neighborhood is proposed to lead the particles track the optimal dimension values of personal best positions and neighborhoods, for avoiding the local convergence. Secondly, a self- adaptive method based on individuals＇ fitness is proposed to adjust the inertia weight in order to improve the searching efficiency of the proposed algorithm. The result on typical test verifies the effectiveness of the proposed method. Finally, the method is applied to PID parameters tuning for typical industrial process control and a satisfactory control effect is obtained.
出处
《自动化技术与应用》
2012年第12期24-27,48,共5页
Techniques of Automation and Applications
关键词
微粒群优化
PID参数整定
邻域最优维值
自适应惯性权重
函数优化
particle swarm optimization
PID parameters tuning
optimal dimension value
self-adaptive inertia weight
functionoptimization
