混沌量子鲸鱼优化算法及其应用  

Chaotic Quantum Whale Optimization Algorithm and Its Application

作　　者：陈志环 梁成龙 董加顺 CHEN Zhi-huan;LIANG Cheng-long;DONG Jia-shun(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China;Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学信息科学与工程学院,湖北武汉430081 [2]武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北武汉430081

出　　处：《计算机仿真》2024年第8期439-446,465,共9页Computer Simulation

基　　金：国家自然科学基金资助项目(62203339,62073250,62003249,62173262);湖北省重点研发计划项目(2020BAB021)。

摘　　要：针对鲸鱼优化算法收敛速度慢和精度低的缺点,提出了一种混沌量子鲸鱼优化算法(Chaotic Quantum Whale Optimization Algorithm, CQWOA)。在上述算法中,首先在种群初始化阶段融入了改进的Tent混沌映射,以提高初始种群多样性;然后在位置更新阶段引入量子旋转门更新策略,提高了算法收敛精度。同时,考虑到双连杆柔性机械臂(Double Link Flexible Manipulator, DLFM)在使用过程中,许多厂商不提供或只提供部分参数,用拉格朗日法建立了DLFM动力学模型,并提出了一套参数辨识策略。通过仿真,可看出提出的CQWOA具有函数寻优效果好,参数辨识精度高的优势。Aiming at the disadvantages of slow convergence speed and low accuracy of the whale optimization algorithm,this paper proposes a Chaotic Quantum Whale Optimization Algorithm(CQWOA).In this algorithm,an improved Tent chaotic map is introduced in the initial stage of the population to improve the diversity of the initial population.Then,in the position update phase,a quantum revolving gate update strategy is introduced to improve the convergence accuracy of the algorithm.At the same time,in view of the fact that many manufacturers do not provide or only provide some parameters during the use of the double link flexible manipulator(DLFM),this paper uses the Lagrangian method to establish the DLFM dynamic model and proposes a set of identification strategies.Through the simulation experiment,it can be seen that CQWOA has the advantages of good function optimization effect and high parameter identification accuracy.

关 键 词：柔性机械臂 鲸鱼优化算法 动力学模型 参数辨识 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]