An Improved Artificial Fish Swarm Algorithm and Its Application

被引：6
|
作者：
Xin, Guan [1 ]
Xin, Yin Yi [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Informat Engn, Beijing 100083, Peoples R China
来源：
MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷
关键词：
artificial fish swarm algorithm; invasive weed optimization; swarm intelligence; PID parameter optimization;
D O I：
10.4028/www.scientific.net/AMR.433-440.4434
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
An improved algorithm (AFSA-IWO) was developed based on the artificial fish swarm algorithm (AFSA) and invasive weed optimization (IWO). It introduces TWO, and improves its mechanism of the competitive exclusion to meet practical application. Convergence analysis was performed with some typical benchmark test functions and comparison was made with AFSA. At the same time, it uses the AFSA-IWO to optimize the PID parameters. The results showed that the approach presented better ability in leaping over the local extremum and enhancing local exploration, and can void blind searching in the later evolution period. So it is a global optimization algorithm with good feasibility and high efficiency.
引用
收藏
页码：4434 / 4438
页数：5
相关论文
共 50 条
  • [41] Improved artificial fish swarm algorithm based on polar coordinate coding
    Song X.-X.
    Sun D.-H.
    Xie J.
    [J]. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2010, 32 (10): : 2248 - 2251
  • [42] The Robot Path Planning Based on Improved Artificial Fish Swarm Algorithm
    Zhang, Yi
    Guan, Guolun
    Pu, Xingchen
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2016, 2016
  • [43] An Improved Cloud Artificial Fish Swarm Algorithm Based on Feedback Mechanism
    Liu, Donglin
    Li, Lele
    Wang, Mingyong
    [J]. 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING APPLICATIONS (CSEA 2015), 2015, : 283 - 288
  • [44] Weapon Target Assignment Based on Improved Artificial Fish Swarm Algorithm
    Ye, Fang
    Shao, Shijia
    Tian, Yuan
    [J]. 2018 USNC-URSI RADIO SCIENCE MEETING (JOINT WITH AP-S SYMPOSIUM), 2018, : 15 - 16
  • [45] A Weights and Improved Adaptive Artificial Fish Swarm Algorithm for Path Planning
    Qi, Baoling
    Xiong, Lingyi
    Wang, Lijun
    Chen, Zhuo
    Huang, Lijia
    [J]. PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019), 2019, : 1698 - 1702
  • [46] The robot path optimization of improved artificial fish-swarm algorithm
    Peng, Jiansheng
    [J]. Computer Modelling and New Technologies, 2014, 18 (06): : 147 - 152
  • [47] An Improved Artificial Fish Swarm Algorithm to Solve the Cutting Stock Problem
    Cheng, Chunying
    Bao, Lanying
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2018, 2018, 10878 : 165 - 172
  • [48] Intelligent Taxi Dispatching Based on Improved Artificial Fish Swarm Algorithm
    Luo, Zhiwei
    Xie, Rong
    Huang, Wangyi
    Shan, Yiwei
    [J]. WEB AND BIG DATA, 2017, 10612 : 94 - 103
  • [49] Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognition
    Yikai Gao
    Linbo Xie
    Zhengdao Zhang
    Qigao Fan
    [J]. Applied Intelligence, 2020, 50 : 2312 - 2327
  • [50] Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognition
    Gao, Yikai
    Xie, Linbo
    Zhang, Zhengdao
    Fan, Qigao
    [J]. APPLIED INTELLIGENCE, 2020, 50 (08) : 2312 - 2327
12345