基于改进量子遗传算法的励磁控制系统PI参数优化
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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.
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.
引文
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[3]Narayanan A,Moore M.Quantum-inspired genetic algorithms[C]//IEEE International Conference on Evolutionary Computation.IEEE,1996:61-66.
[4]Prakash S,Vidyarthi D P.A novel scheduling model for computational grid using quantum genetic algorithm[J].Journal of Supercomputing,2013,65(2):742-770.
[5]Liu B,Tang L,Wang J,et al.2-D defect profile reconstruction from ultrasonic guided wave signals based on QGA-kernelized ELM[J].Neurocomputing,2014,128(5):217-223.
[6]张羽,邓兆祥,张河山,等.量子遗传算法在永磁同步轮毂电机优化设计中的应用[J].重庆大学学报:自然科学版,2017(8):1-8.
[7]张斌,苏道磊,范建柯,等.基于自适应量子遗传算法对胶东半岛地区乳山震群重定位及构造特征分析[J].地球物理学进展,2017,32(3):1080-1088.
[8]周仕平,陈权,胡存刚,等.基于量子遗传算法的NPC三电平调制策略研究[J].电力电子技术,2017(3):9-11.
[9]李文敬,李沛武.基于IQPSO优化SVM在径流预报中的应用[J].南昌工程学院学报,2018,37(03):58-63.
[10]Han K H,Kim J H.Genetic quantum algorithm and its application to combinatorial optimization problem[C]//Evolutionary Computation,2000.Proceedings of the 2000 Congress on.IEEE,2002:1354-1360 vol.2.
[11]何颖,张海丽,石黄霞,等.改进量子遗传算法辨识超混沌系统[J].量子电子学报,2016,33(5):578-583.
[12]于(王乐),汪家权.改进的QGA-BP模型在复杂水质预测中的应用[J].模式识别与人工智能,2012,25(4):705-708.
[13]Sun Y,Xiong H.Real coded quantum genetic algorithm and its application[J].Journal of Engineering Science&Technology Review,2013,6(5):25-32.
[14]Wei X K,Shao W,Zhang C,et al.Improved self-adaptive genetic algorithm with quantum scheme for electromagnetic optimisation[J].Microwaves Antennas&Propagation Iet,2014,8(12):965-972.
[15]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,1995.Proceedings.IEEE,2002:1942-1948 vol.4.
[16]Ogata K.Modern Control Engineering[M].Publishing House of Elec,2011.