摘要
针对传统的量子遗传算法(QGA)需要根据具体的问题选择合适的量子旋转门来更新量子比特的状态,提出了一种无需量子门、通用的、与问题无关的改进量子遗传算法(IQGA)。在采用实数编码的量子遗传算法的基础上,结合粒子群优化算法更新量子比特的状态,代替了传统量子遗传算法用量子门更新量子比特,避免了传统量子遗传算法复杂二进制编码和解码过程,增强了量子遗传算法的使用范围。最后,将提出的IQGA应用到某水电站励磁控制系统的PI参数优化,与遗传算法(GA)、QGA进行了仿真对比分析,结果表明IQGA鲁棒性最强,算法运行时间比传统量子遗传算法时间大约缩短了8s,优化所得的PI参数用于励磁控制系统的性能最佳。
In view of the fact that the state of the qubit needs to be updated to select the appropriate quantum revolving door according to the specific problem with the traditional quantum genetic algorithm( QGA),an improved universal and question-independent quantum genetic algorithm( IQGA),which does not need quantum gate,was proposed. Based on real coded quantum genetic algorithm,we use the particle swarm optimization algorithm to update the state of the qubit,replacing the traditional quantum genetic algorithm with quantum gate to update the qubit,avoiding the complex binary encoding and decoding process of the traditional quantum genetic algorithm,increasing the use range of quantum genetic algorithm. Finally,IQGA proposed in this paper was used in optimization of PI parameters of excitation control system of a hydropower station.Compared with genetic algorithm( GA) and traditional quantum genetic algorithm( QGA),the results show that IQGA proposed has the strongest robustness,that the running time of the algorithm is shortened by about 8 s compared with the traditional quantum genetic algorithm,and that the optimized PI parameters are the best for the excitation control system.
作者
朱能飞
王翠
崔晓斌
徐键
ZHU Nengfei;WANG Cui;CUI Xiaobin;XU Jian(School of Mechanical and Electrical Engineering,Nanchang Institute of Technology,Nanchang 330099,China)
出处
《南昌工程学院学报》
CAS
2019年第1期91-97,共7页
Journal of Nanchang Institute of Technology
基金
国家自然科学基金资助项目(51667015)
南昌工程学院研究生创新项目(YJSCX20170021)
关键词
励磁控制系统
PI参数
改进实数量子遗传算法
量子遗传算法
遗传算法
excitation control systems
PI parameter
improved real coded quantum genetic algorithm
quantum genetic algorithm
genetic algorithm
作者简介
朱能飞(1993-),男,硕士生,nengfeizhu@163.com.
