Choosing suitable variants of Differential Evolution and Particle Swarm Optimization for the optimization of a PI cascade control

被引:0
|
作者
Zielinski, K. [1 ]
Joost, M. [2 ]
Laur, R. [1 ]
Orlik, B. [2 ]
机构
[1] Univ Bremen, ITEM, D-2800 Bremen 33, Germany
[2] Univ Bremen, Inst Elect Drives Power Elect & Devices IALB, D-2800 Bremen 33, Germany
关键词
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Robust control is an intensively studied field in control theory where analytical solutions for robust control problem, often lead to complicated control structures. On the other hand, for simple structures like the PI cascade control there are no analytical solutions which leads to the need of easy-to-use optimization algorithms. Differential Evolution and Particle Swarm Optimization are well suited for this problem because of the real-valued representation of solutions, fast convergence behavior and ease of use. however, several variants exist for both algorithms, and from literature it does not become clear which one performs best. Therefore, in this paper several strategies of Differentia) E volution and different neighborhood topologies for Particle Swarm Optimization are applied for the optimization of a PI cascade control. A performance comparison shows that both algorithms are able to solve this optimization problem but especially using Differential Evolution the quality of the best solution varies for different strategies.
引用
收藏
页码:55 / +
页数:2
相关论文
共 50 条
  • [21] Hybrid Differential Evolution Particle Swarm Optimization Algorithm for Reactive Power Optimization
    Wang, Shouzheng
    Ma, Lixin
    Sun, Dashuai
    2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2010,
  • [22] A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization
    Zhang, Changsheng
    Ning, Jiaxu
    Lu, Shuai
    Ouyang, Dantong
    Ding, Tienan
    OPERATIONS RESEARCH LETTERS, 2009, 37 (02) : 117 - 122
  • [23] Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization
    Liu, Hui
    Cai, Zixing
    Wang, Yong
    APPLIED SOFT COMPUTING, 2010, 10 (02) : 629 - 640
  • [24] Optimization of Wireless Sensor Node Parameters by Differential Evolution and Particle Swarm Optimization
    Kroemer, Pavel
    Prauzek, Michal
    Musilek, Petr
    Barton, Tomas
    PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS (IBICA 2014), 2014, 303 : 13 - 22
  • [25] Choosing a starting configuration for particle swarm optimization
    Richards, M
    Ventura, D
    2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2004, : 2309 - 2312
  • [26] An Overview of Particle Swarm Optimization Variants
    Imran, Muhammad
    Hashim, Rathiah
    Abd Khalid, Noor Elaiza
    MALAYSIAN TECHNICAL UNIVERSITIES CONFERENCE ON ENGINEERING & TECHNOLOGY 2012 (MUCET 2012), 2013, 53 : 491 - 496
  • [27] Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization
    Baioletti, Marco
    Milani, Alfredo
    Santucci, Valentino
    Tomassini, Marco
    APPLICATIONS OF EVOLUTIONARY COMPUTATION (EVOAPPLICATIONS 2022), 2022, : 46 - 60
  • [28] Hybridizing particle swarm optimization with simulated annealing and differential evolution
    Mirsadeghi, Emad
    Khodayifar, Salman
    Cluster Computing, 2021, 24 (02): : 1135 - 1163
  • [29] Comparison between Differential Evolution and Particle Swarm Optimization Algorithms
    Zhang, Dan
    Wei, Bin
    2014 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2014), 2014, : 239 - 244
  • [30] Hybrid algorithm based on particle swarm optimization and differential evolution
    Yu, Yufeng
    Xu, Chen
    Li, Guo
    Li, Jingwen
    Journal of Computational Information Systems, 2014, 10 (11): : 4619 - 4627