摘要
针对量子遗传算法中旋转变异角固定的缺点,提出了一种旋转变异角幅度随着个体适应度的变化而自适应调整的自适应量子遗传算法,对典型测试函数的优化结果表明了该策略能有效提高量子遗传算法的优化精度和收敛速度。同时,针对分数阶PIλDμ控制器难于整定的问题,将参数整定转化为参数优化,提出采用AQGA对分数阶PIλDμ参数进行优化,并利用该策略对循环流化床主汽温分数阶系统设计了分数阶PIλDμ,仿真结果表明了该方法在PIλDμ参数整定中的有效性。
Aiming at the disadvantage of the immobile rotated angle in quantum genetic algorithm （QGA）, the adap- tive quantum genetic algorithm （AQGA） is figured out, whose rotated angle is automatically adjusted as the change of individual fitness. The optimization results of typical test function show that the precision and convergence speed can be effectively improved by AQGA. In addition, for the difficulty of PIλDμ parameters tuning, AQGA is applicated. A sim- ulation is carried out for circulating fluidized bed steam temperature system whose model is with fractional orders. And PIeD~ parameters are respectively tuned by QGA and AQGA. The simulation results show that AQGA is an effective method in PISD~ parameters tuning.
出处
《华北电力大学学报（自然科学版）》
CAS
北大核心
2014年第2期72-77,共6页
Journal of North China Electric Power University：Natural Science Edition
基金
南京工程学院校级青年基金项目(QKJB201301)
关键词
自适应量子遗传算法
分数阶PIλDμ
参数整定
循环流化床主汽温
分数阶对象
adaptive quantum genetic algorithm （AQGA）
fractional order PIλDμ
parameters tuning
circulatingfluidized bed steam temperature
fractional order controlled object
