Hybrid constrained genetic algorithm/particle swarm optimisation load flow algorithm

被引：21
|
作者：
Ting, T. O. [1 ]
Wong, K. P. [1 ,2 ]
Chung, C. Y. [1 ]
机构：
[1] Hong Kong Polytech Univ, Dept Elect Engn, Computat Intelligence Applicat Res Lab, Kowloon, Hong Kong, Peoples R China
[2] Univ Western Australia, Sch Elect Elect & Comp Engn EECE, Crawley, WA 6009, Australia
来源：
IET GENERATION TRANSMISSION & DISTRIBUTION | 2008年 / 2卷 / 06期
关键词：
D O I：
10.1049/iet-gtd:20070224
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A hybrid constrained genetic algorithm and particle swarm optimisation (PSO) method for the evaluation of the load flow in heavy-loaded power systems is developed. The new algorithm is applied to find the maximum loading points of three IEEE test systems. The experimental determination of the best values of the parameters for use in the PSO part of the hybrid algorithm is also reported.
引用
收藏
页码：800 / 812
页数：13
相关论文
共 50 条
  • [1] Constrained optimization by the ε constrained hybrid algorithm of particle swarm optimization and genetic algorithm
    Takahama, T
    Sakai, S
    Iwane, N
    [J]. AI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2005, 3809 : 389 - 400
  • [2] A Dynamic Neighbourhood Particle Swarm Optimisation Algorithm for Constrained Optimisation
    Li, Lily D.
    Yu, Xinghuo
    Li, Xiaodong
    Guo, William
    [J]. IECON 2011: 37TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2011,
  • [3] A hybrid particle swarm optimisation-genetic algorithm applied to grid scheduling
    Higashino, Wilson A.
    Capretz, Miriam A. M.
    de Toledo, M. Beatriz F.
    Bittencourt, Luiz F.
    [J]. INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING, 2016, 7 (02) : 113 - 129
  • [4] Assembly sequence planning based on a hybrid particle swarm optimisation and genetic algorithm
    Xing, Yanfeng
    Wang, Yansong
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2012, 50 (24) : 7303 - 7312
  • [5] Bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study
    Sadeghiram, Soheila
    [J]. INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2017, 10 (04) : 275 - 282
  • [6] Hybrid particle swarm optimisation algorithm for image segmentation
    Zhang, Jian-de
    Lu, Jin-gui
    Li, Hong-liang
    [J]. INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2011, 14 (04) : 317 - 323
  • [7] A Hybrid Particle Swarm Genetic Algorithm for Classification
    Ding, Rui
    Dong, Hongbin
    Feng, Xianbin
    Yin, Guisheng
    [J]. INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL SCIENCES AND OPTIMIZATION, VOL 2, PROCEEDINGS, 2009, : 301 - +
  • [8] A Comparative Study of Genetic Algorithm and Particle Swarm Optimisation for Dendritic Cell Algorithm
    Elisa, Noe
    Yang, Longzhi
    Chao, Fei
    Naik, Nitin
    [J]. 2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2020,
  • [9] A hybrid cooperative cuckoo search algorithm with particle swarm optimisation
    Wang, Lijin
    Zhong, Yiwen
    Yin, Yilong
    [J]. INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2015, 6 (01) : 18 - 29
  • [10] A Hybrid Genetic Algorithm and Particle Swarm Optimization for Flow Shop Scheduling Problems
    Alvarez Pomar, Lindsay
    Cruz Pulido, Elizabeth
    Tovar Roa, Julian Dario
    [J]. APPLIED COMPUTER SCIENCES IN ENGINEERING, 2017, 742 : 601 - 612
12345