摘要
提出一种改进的粒子群优化算法,除了个体极值和全局极值外,改进算法中还引入了粒子群的平均位置。因此,粒子可以获得更多的信息来调整自身的状态。基于3个基准测试函数的测试结果显示改进粒子群优化算法具有较好的全局收敛性和收敛精度。计算机仿真结果表明:改进粒子群优化算法应用于水轮机控制器PID参数的优化设计可以有效地改善水轮机控制系统过渡过程的动态性能。
An improved particle swarm optimization (PSO) algorithm is presented. Besides the individual best position and the global best position, the swarm average position is introduced in the improved PSO algorithm. Therefore, more information is acquired by particles to adjust their movements. The test results based on three benchmark functions show that the improved PSO algorithm has a good performance on global convergency and convergence precision. The computer simulation results indicate that the application of the improved PSO algorithm in hydraulic turbine governor PID parameters optimization can effectively improve the dynamic performance of hydraulic transients.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2008年第3期274-278,共5页
Journal of Nanjing University of Science and Technology
基金
河海大学自然科学基金(2007418211)
关键词
粒子群优化
群智能
水轮机控制器
参数优化
particle swarm optimization
swarm intelligence
hydraulic turbine governor
parameters optimization