野草猴群算法的传感器优化布置方法研究
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摘要
简易猴群算法存在初始化分布随机、爬步长固定、优秀猴子特征信息不能传承等缺陷,使算法求解性能受限。为解决以上问题,提出了一种用于传感器优化布置的野草猴群算法。该算法利用正态分布方法提高初始种群的多样性;采取自适应爬步长提升求解速度和搜索精度;融入野草繁殖进化和竞争排斥机制,扩大后代种群繁殖中优秀猴子的影响范围。以常用的模态置信度矩阵为传感器优化布置的目标函数,配置传感器的布置位置。以常用的8个测试函数和3个常用算法对其进行分析,验证了算法的可行性和有效性。最后以糊底机涂胶机构为例,进行传感器优化布置方案选择。实验结果表明,野草猴群算法的求解精度较简易猴群算法有大幅提高。
The simple monkey algorithm has the shortcomings of initial distribution randomization,fixed climb step length and incapability of inheriting the characteristics of excellent monkeys,which limits the solution performance of the algorithm.In order to solve the above problems,a weed monkey algorithm for optimal sensor placement is proposed.Normal distribution is used to enhance the diversity of initial monkey populations.Self-adaptive climb step is introduced to improve the solution accuracy and convergence rate.Both weed reproduction evolution and competitive exclusion mechanism are used to enlarge the influence of excellent monkey on the monkey offspring population.The commonly used Modal Assurance Criterion(MAC)is used as the objective function of optimal sensor placement.The commonly used 8 test functions and 3 algorithms are used to verify the feasibility and effectiveness of the algorithm.Finally,the optimal sensor placement is carried out on the gelatinize mechanism of bag bottompasting machine.The results show that,compared with the simple monkey algorithm,the solution accuracy of the weed monkey algorithm precision is greatly improved.
The simple monkey algorithm has the shortcomings of initial distribution randomization,fixed climb step length and incapability of inheriting the characteristics of excellent monkeys,which limits the solution performance of the algorithm.In order to solve the above problems,a weed monkey algorithm for optimal sensor placement is proposed.Normal distribution is used to enhance the diversity of initial monkey populations.Self-adaptive climb step is introduced to improve the solution accuracy and convergence rate.Both weed reproduction evolution and competitive exclusion mechanism are used to enlarge the influence of excellent monkey on the monkey offspring population.The commonly used Modal Assurance Criterion(MAC)is used as the objective function of optimal sensor placement.The commonly used 8 test functions and 3 algorithms are used to verify the feasibility and effectiveness of the algorithm.Finally,the optimal sensor placement is carried out on the gelatinize mechanism of bag bottompasting machine.The results show that,compared with the simple monkey algorithm,the solution accuracy of the weed monkey algorithm precision is greatly improved.
引文
[1]Yi Ting-hua,Zhang Xu-dong,Li Hong-nan.Collaborativeclimb monkey algorithm for optimal sensor placement[J].Chinese Journal of Computational Mechanics,2014,31(6):681-686.(in Chinese)
[2]Mehrabian A R,Lucas C.A novel numerical optimization algorithm inspired from weed colonization[J].Ecological Informatics,2006,1(4):355-366.
[3]Han Yi,Cai Jian-hu,Li Yan-lai,et al.Invasive weed optimization and its advances[J].Computer Science,2011,38(3):20-23.(in Chinese)
[4]Zhao Rui-qing,Tang Wan-sheng.Monkey algorithm for global numerical optimization[J].Journal of Uncertain System,2008,2(3):164-175.
[5]Zhang Jia-jia.An intrusion detection method based on monkey algorithm[D].Tianjin:Tianjin University,2010.(in Chinese)
[6]Wang Jing-ran,Yu Yi-xin,Zeng Huan.Discrete monkey algorithm and its application in transmission network expansion planning[J].Journal of Tianjin University,2010,43(9):798-803.(in Chinese)
[7]Yi Ting-hua,Li Hong-nan,Zhang Xu-dong.A modified monkey algorithm for optimal sensor placement in structural health monitoring[J].Smart Materials and Structures,2012,21(10):article ID 105033.
[8]Zhang Xu-dong.Optimal sensor placement based on monkey algorithm[D].Dalian:Dalian University of Technology,2013.(in Chinese)
[9]Zhang Ya-jie.Research on monkey algorithm and its application[D].Xi’an:Xidian University,2014.(in Chinese)
[10]Du Guo-zhang,Ma Li.Application of improved monkey algorithm in optimal placement of sensor[J].Transducer and Microsystem Technologies,2015,34(8):152-155.(in Chinese)
[11]Came T G,Dohmann C R.A modal test design strategy for modal correlation[C]∥Proc of the 13th International Modal Analysis Conference,1995:927-933.
[12]Wei Xiu-ye,Pan Hong-xia,Huang Jin-ying.Study on sensor optimal layout for gearbox[J].Acta Armamentarii,2010,31(11):1508-1513.(in Chinese)
[13]Liu Yan,Liu Gui-jie,Liu Bo.Research status and prospect on optimal placement of sensor[J].Transducer and Microsystem Technologies,2010,29(11):4-13.(in Chinese)
[14]Han Jian-quan,Mao Li,Zhou Chang-xi.Artificial bee colony algorithm based on improved local search strategy[J].Journal of Frontiers of Computer Science and Technology,2015,9(6):761-767.(in Chinese)
[15]Xu Bin.Research and application of multi-objective optimization algorithms based on differential evolution[D].Shanghai:East China University of Science and Technology,2013.(in Chinese)
[16]Xu He-ming.Research on multi-objective particle swarm optimization algorithms[D].Shanghai:Shanghai Jiao Tong University,2013.(in Chinese)
[17]Wang Yi-bing,Peng Zhen-rui,Yin Hong,et al.Design of the control system for paper and yarn compounded bag bottompasting machine[J].Manufacturing Automation,2015,37(1):123-126.(in Chinese)
[18]Zhao Yu.Optimal sensor placement for bridge structural health monitoring based on intelligence algorithm[D].Lanzhou:Lanzhou Jiaotong University,2014.(in Chinese)
[19]Bansal J C,Sharma H,Jadon S S.Spider monkey optimization algorithm for numerical optimization[J].Memetic Computing,2014,6(1):31-47.
[20]Yi Ting-hua,Li Hong-nan,Zhang Xu-dong.Health monitoring sensor placement optimization for canton tower using immune monkey algorithm[J].Structural Control and Health Monitoring,2015,22(1):123-138.
[1]伊廷华,张旭东,李宏男.基于协同爬猴群算法的传感器优化布置方法研究[J].计算力学学报,2014,31(6):681-686.
[3]韩毅,蔡建湖,李廷来,等.野草算法及其研究进展[J].计算机科学,2011,38(3):20-23.
[5]张佳佳.基于猴群算法的入侵检测技术研究[D].天津:天津大学,2010.
[6]王靖然,余贻鑫,曾浣.离散猴群算法及其在输电网扩展规划中的应用[J].天津大学学报,2010,43(9):798-803.
[8]张旭东.基于猴群算法的传感器优化布置方法研究[D].大连:大连理工大学,2013.
[9]张亚洁.猴群算法及其应用研究[D].西安:西安电子科技大学,2014.
[10]杜国璋,马丽.改进的猴群算法在传感器优化布置中的应用[J].传感器与微系统,2015,34(8):152-155.
[12]魏秀业,潘宏侠,黄晋英.齿轮箱传感器优化布置研究[J].兵工学报,2010,31(11):1508-1513.
[13]刘艳,刘贵杰,刘波.传感器优化布置研究现状与展望[J].传感器与微系统,2010,29(11):4-13.
[14]韩建权,毛力,周长喜.基于改进局部搜索策略的人工蜂群算法[J].计算机科学与探索,2015,9(6):761-767.
[15]徐斌.基于差分进化算法的多目标优化方法研究及其应用[D].上海:华东理工大学,2013.
[16]徐鹤鸣.多目标粒子群优化算法的研究[D].上海:上海交通大学,2013.
[17]王义兵,彭珍瑞,殷红,等.纸纱复合袋糊底机控制系统设计[J].制造业自动化,2015,37(1):123-126.
[18]赵宇.基于智能算法的桥梁结构健康监测传感器优化配置研究[D].兰州:兰州交通大学,2014.
[2]Mehrabian A R,Lucas C.A novel numerical optimization algorithm inspired from weed colonization[J].Ecological Informatics,2006,1(4):355-366.
[3]Han Yi,Cai Jian-hu,Li Yan-lai,et al.Invasive weed optimization and its advances[J].Computer Science,2011,38(3):20-23.(in Chinese)
[4]Zhao Rui-qing,Tang Wan-sheng.Monkey algorithm for global numerical optimization[J].Journal of Uncertain System,2008,2(3):164-175.
[5]Zhang Jia-jia.An intrusion detection method based on monkey algorithm[D].Tianjin:Tianjin University,2010.(in Chinese)
[6]Wang Jing-ran,Yu Yi-xin,Zeng Huan.Discrete monkey algorithm and its application in transmission network expansion planning[J].Journal of Tianjin University,2010,43(9):798-803.(in Chinese)
[7]Yi Ting-hua,Li Hong-nan,Zhang Xu-dong.A modified monkey algorithm for optimal sensor placement in structural health monitoring[J].Smart Materials and Structures,2012,21(10):article ID 105033.
[8]Zhang Xu-dong.Optimal sensor placement based on monkey algorithm[D].Dalian:Dalian University of Technology,2013.(in Chinese)
[9]Zhang Ya-jie.Research on monkey algorithm and its application[D].Xi’an:Xidian University,2014.(in Chinese)
[10]Du Guo-zhang,Ma Li.Application of improved monkey algorithm in optimal placement of sensor[J].Transducer and Microsystem Technologies,2015,34(8):152-155.(in Chinese)
[11]Came T G,Dohmann C R.A modal test design strategy for modal correlation[C]∥Proc of the 13th International Modal Analysis Conference,1995:927-933.
[12]Wei Xiu-ye,Pan Hong-xia,Huang Jin-ying.Study on sensor optimal layout for gearbox[J].Acta Armamentarii,2010,31(11):1508-1513.(in Chinese)
[13]Liu Yan,Liu Gui-jie,Liu Bo.Research status and prospect on optimal placement of sensor[J].Transducer and Microsystem Technologies,2010,29(11):4-13.(in Chinese)
[14]Han Jian-quan,Mao Li,Zhou Chang-xi.Artificial bee colony algorithm based on improved local search strategy[J].Journal of Frontiers of Computer Science and Technology,2015,9(6):761-767.(in Chinese)
[15]Xu Bin.Research and application of multi-objective optimization algorithms based on differential evolution[D].Shanghai:East China University of Science and Technology,2013.(in Chinese)
[16]Xu He-ming.Research on multi-objective particle swarm optimization algorithms[D].Shanghai:Shanghai Jiao Tong University,2013.(in Chinese)
[17]Wang Yi-bing,Peng Zhen-rui,Yin Hong,et al.Design of the control system for paper and yarn compounded bag bottompasting machine[J].Manufacturing Automation,2015,37(1):123-126.(in Chinese)
[18]Zhao Yu.Optimal sensor placement for bridge structural health monitoring based on intelligence algorithm[D].Lanzhou:Lanzhou Jiaotong University,2014.(in Chinese)
[19]Bansal J C,Sharma H,Jadon S S.Spider monkey optimization algorithm for numerical optimization[J].Memetic Computing,2014,6(1):31-47.
[20]Yi Ting-hua,Li Hong-nan,Zhang Xu-dong.Health monitoring sensor placement optimization for canton tower using immune monkey algorithm[J].Structural Control and Health Monitoring,2015,22(1):123-138.
[1]伊廷华,张旭东,李宏男.基于协同爬猴群算法的传感器优化布置方法研究[J].计算力学学报,2014,31(6):681-686.
[3]韩毅,蔡建湖,李廷来,等.野草算法及其研究进展[J].计算机科学,2011,38(3):20-23.
[5]张佳佳.基于猴群算法的入侵检测技术研究[D].天津:天津大学,2010.
[6]王靖然,余贻鑫,曾浣.离散猴群算法及其在输电网扩展规划中的应用[J].天津大学学报,2010,43(9):798-803.
[8]张旭东.基于猴群算法的传感器优化布置方法研究[D].大连:大连理工大学,2013.
[9]张亚洁.猴群算法及其应用研究[D].西安:西安电子科技大学,2014.
[10]杜国璋,马丽.改进的猴群算法在传感器优化布置中的应用[J].传感器与微系统,2015,34(8):152-155.
[12]魏秀业,潘宏侠,黄晋英.齿轮箱传感器优化布置研究[J].兵工学报,2010,31(11):1508-1513.
[13]刘艳,刘贵杰,刘波.传感器优化布置研究现状与展望[J].传感器与微系统,2010,29(11):4-13.
[14]韩建权,毛力,周长喜.基于改进局部搜索策略的人工蜂群算法[J].计算机科学与探索,2015,9(6):761-767.
[15]徐斌.基于差分进化算法的多目标优化方法研究及其应用[D].上海:华东理工大学,2013.
[16]徐鹤鸣.多目标粒子群优化算法的研究[D].上海:上海交通大学,2013.
[17]王义兵,彭珍瑞,殷红,等.纸纱复合袋糊底机控制系统设计[J].制造业自动化,2015,37(1):123-126.
[18]赵宇.基于智能算法的桥梁结构健康监测传感器优化配置研究[D].兰州:兰州交通大学,2014.