[1]王 齐,陈 娟,李全善,等.改进生物地理学优化算法及其在PID控制器参数中的优化[J].南京理工大学学报(自然科学版),2017,41(04):519.[doi:10.14177/j.cnki.32-1397n.2017.41.04.018]
　Wang Qi,Chen Juan,Li Quanshan,et al.PID parameter optimization based on improvedbiogeography-based optimization algorithm[J].Journal of Nanjing University of Science and Technology,2017,41(04):519.[doi:10.14177/j.cnki.32-1397n.2017.41.04.018]
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改进生物地理学优化算法及其在PID控制器参数中的优化()
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《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年04期
页码:
519
栏目:
出版日期:
2017-08-31

文章信息/Info

Title:
PID parameter optimization based on improvedbiogeography-based optimization algorithm
文章编号:
1005-9830(2017)04-0519-07
作者:
王 齐1; 陈 娟1; 李全善2; 刘继超3
1.北京化工大学 信息科学与技术学院,北京 100029; 2.北京世纪隆博科技有限责任公司,北京 100029; 3.燕京理工学院,河北 三河 065201
Author(s):
Wang Qi1; Chen Juan1; Li Quanshan2; Liu Jichao3
1.School of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China; 2.Beijing Century Robust Technology Co.Ltd.,Beijing 100029,China; 3.Yanching Institute of Technology,Sanhe 065201,China
关键词:
人工驯养;  生物地理学;  优化算法;  参数优化
Keywords:
artificial domestication;  biogeography;  optimization algorithm;  parameter optimization
分类号:
TP273.24
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.018
摘要:
为了解决生物地理学优化(Biogeography-based optimization,BBO)算法在收敛过程容易陷入早熟的问题,提出了一种改进的生物地理学优化(Improved biogeography-based optimization,I-BBO)算法。该算法是在生物地理学的基础上,引入了人工驯养的概念,把人工驯化与生物地理学算法相结合,解决了BBO算法在后期存在搜索动力不足的问题。仿真实验表明:I-BBO算法提高了物种的多样性,增强了算法的搜索能力,加快了寻优速度。将该文提出的I-BBO算法应用到比例积分微分(Proportion integration differentiation,PID)控制器的参数整定中,通过两个例子的仿真,结果表明,I-BBO算法在优化PID控制器参数上比原有的BBO算法更加迅速。
Abstract:
An improved biogeography-based optimization(I-BBO)algorithm is presented in order to solve the problem of biogeography-based optimization(BBO)algorithm,which has the phenomenon of easily precocious convergence in the process of optimization.Based on the biogeography,the algorithm introduces the concept of artificial domestication and the integrating artificial domestication in biogeography algorithm to solve the problem of the less ability to explore in the end of the process.Simulation experiments prove that the I-BBO algorithm improves the species diversity and enhances the search ability of the algorithm.The I-BBO algorithm is applied to set the proportion-integration-differentiation(PID)controller parameters.The simulation results of the two examples show that the I-BBO algorithm is more rapid than the BBO algorithm in the PID controller parameters optimization.

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备注/Memo

备注/Memo:
收稿日期:2016-10-10 修回日期:2017-01-16基金项目:国家自然科学基金(21376014)
作者简介:王齐(1990-),男,硕士生,主要研究方向:系统辨识、先进控制,E-mail:wangqi_buct@foxmail.com; 通讯作者:陈娟(1961-),女,教授,博导,主要研究方向:控制理论及先进控制方法、系统辨识建模等,E-mail:jchen@mail.buct.edu.cn。
引文格式:王齐,陈娟,李全善,等.改进生物地理学优化算法及其在PID控制器参数中的优化[J].南京理工大学学报,2017,41(4):519-525.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-08-31