Modified parallel nested-layer particle swarm optimization algorithm for fast bifurcation point detection and its software implementation

被引:0
|
作者
Hasegawa, Tomo [1 ]
Matsushita, Haruna [2 ]
Kousaka, Takuji [3 ]
Kurokawa, Hiroaki [1 ]
机构
[1] Tokyo Univ Technol, Dept Elect & Elect Engn, 1404-1 Katakuramachi, Hachioji, Tokyo 1920983, Japan
[2] Kagawa Univ, Dept Elect & Informat Engn, 2217-20 Hayashi Cho, Takamatsu, Kagawa 7610396, Japan
[3] Chukyo Univ, Dept Elect & Elect Engn, 101-2 Yagoto Honmachi,Showa Ku, Nagoya, Aichi 4668666, Japan
来源
关键词
bifurcation point detection; particle swarm optimization; parallel computing; OpenMP; COMPUTATION; VALUES;
D O I
10.1587/nolta.14.308
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper reports on the development of straightforward software for bifurcation point detection in discrete-time dynamical systems with a fast and versatile algorithm. Nested -layer particle swarm optimization (NLPSO) is an effective general-purpose bifurcation point detection strategy. In contrast to traditional gradient-based methods, the NLPSO method does not require derivatives of the objective functions. Note that NLPSO can incur high time costs, and parallel computation is practical. The software reported in this study employs modified NLPSO that is optimized for parallel computing. The proposed algorithm is fast and easy to use with limited knowledge of bifurcation point detection, algorithms, or parallel computing techniques.
引用
收藏
页码:308 / 318
页数:11
相关论文
共 50 条
  • [31] Parallel Particle Swarm Optimization Algorithm and Its Application in the Optimal Operation of Cascade Reservoirs in Yalong River
    Chen Lihua
    Mei Yadong
    Yang Na
    ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL I, PROCEEDINGS, 2009, : 279 - 282
  • [32] Detection of rice type and its storage duration via an improved particle swarm optimization algorithm
    Rahimzadeh, Hassan
    Sadeghi, Morteza
    Mireei, Seyed Ahmad
    Ghasemi-Varnamkhasti, Mahdi
    EVOLUTIONARY INTELLIGENCE, 2024, 17 (5-6) : 3291 - 3301
  • [33] A Modified Mutation-Dissipation Binary Particle Swarm Optimization Algorithm and Its Application to WFGD Control
    Li, Hongxing
    Wang, Ling
    Wang, Ling
    Zhen, LanLan
    Zhen, LanLan
    Huang, Ziyuan
    ISISE 2008: INTERNATIONAL SYMPOSIUM ON INFORMATION SCIENCE AND ENGINEERING, VOL 2, 2008, : 258 - +
  • [34] Design optimization of aerodynamic shapes of a wing and its winglet using modified quantum-behaved particle swarm optimization algorithm
    Zhang Wei
    Sun Meijian
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2014, 228 (09) : 1638 - 1647
  • [35] A Modified Particle Swarm Optimization and Radial Basis Function Neural Network Hybrid Algorithm Model and Its Application
    Shi, Biao
    Li Yu-xia
    Wang, Yan
    Peng, Li
    Xin, Meng
    Yu Xin-hua
    PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL I, 2009, : 134 - +
  • [36] Decision-making model for transportation and distribution of emergency materials and its modified particle swarm optimization algorithm
    Pang, Hai-Yun
    Liu, Nan
    Wu, Qiao
    Kongzhi yu Juece/Control and Decision, 2012, 27 (06): : 871 - 874
  • [37] An improved particle swarm optimization algorithm and its application in solving forward kinematics of a 3-DoF parallel manipulator
    Zhang, Shuzhen
    Yuan, Xiaolong
    Docherty, Paul D.
    Yang, Kai
    Li, Chunling
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2021, 235 (05) : 896 - 907
  • [38] Implementation of a Low Cost Interfering Signal Cancellation Approach Based on a Fast Power Minimization Technique Using Particle Swarm Optimization Algorithm
    Tamjid, Farshid
    Quaiyum, Farhan
    Kvelashvili, Tsotne
    Kazemi, Robab
    Nghia Tran
    Kilic, Ozlem
    Fathy, Aly E.
    2020 IEEE RADIO AND WIRELESS SYMPOSIUM (RWS 2020), 2020, : 116 - 118
  • [39] Emergency supplies scheduling model for single demand point and its constrained multi-objective particle swarm optimization algorithm
    Lin, Yong
    Jiang, Dali
    Zhang, Li
    Wang, Yisheng
    BioTechnology: An Indian Journal, 2014, 10 (09) : 3856 - 3867
  • [40] Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study
    Ma, Yuliang
    Zhang, Songjie
    Qi, Donglian
    Luo, Zhizeng
    Li, Rihui
    Potter, Thomas
    Zhang, Yingchun
    ELECTRONICS, 2020, 9 (05)