中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
地质与地球物理研究所 [2]
过程工程研究所 [2]
长春光学精密机械与物... [1]
自动化研究所 [1]
生态环境研究中心 [1]
工程热物理研究所 [1]
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OAI收割 [8]
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期刊论文 [6]
会议论文 [2]
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2021 [1]
2020 [1]
2018 [2]
2012 [1]
2011 [2]
2007 [1]
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A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems
期刊论文
OAI收割
SUSTAINABILITY, 2021, 卷号: 13, 期号: 19, 页码: 27
作者:
Lodhi, Ehtisham
;
Wang, Fei-Yue
;
Xiong, Gang
;
Mallah, Ghulam Ali
;
Javed, Muhammad Yaqoob
  |  
收藏
  |  
浏览/下载:29/0
  |  
提交时间:2022/01/27
photovoltaic (PV)
partial shading
maximum power point tracking (MPPT)
dragonfly optimization algorithm (DOA)
adaptive cuckoo search optimization (ACSO)
fruit fly optimization algorithm combined with general regression neural network (FFO-GRNN)
improved particle swarm optimization (IPSO)
voltage source inverter (VSI)
total harmonic distortion (THD)
Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction
期刊论文
OAI收割
WATER RESOURCES MANAGEMENT, 2020, 卷号: 34, 期号: 11, 页码: 3515-3531
作者:
Jiang, Yan
;
Bao, Xin
;
Hao, Shaonan
;
Zhao, Hongtao
;
Li, Xuyong
  |  
收藏
  |  
浏览/下载:22/0
  |  
提交时间:2021/08/31
Streamflow prediction
Chaohe River basin
Chaotic dynamic characteristics
Phase space reconstruction
Extreme learning machine
Improved particle swarm optimization algorithm
Multi-objective Optimization of the Energy System in an Iron and Steel Plant for Energy Saving and Low Emissions
会议论文
OAI收割
Graz, AUSTRIA, JUN 10-13, 2018
作者:
Zeng, Yujiao
;
Xiao, Xin
;
Li, Jie
;
Song, Fei
  |  
收藏
  |  
浏览/下载:32/0
  |  
提交时间:2019/06/21
multi-objective optimization
POWER
environmental impact
MODEL
integrated energy system
improved multi-objective particle swarm
iron and steel
Multi-objective Optimization of the Energy System in an Iron and Steel Plant for Energy Saving and Low Emissions
期刊论文
OAI收割
28TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, 2018, 卷号: 43, 页码: 1141, 1146
作者:
Zeng, YJ
;
Xiao, X
;
Li, J
;
Song, F
;
Zeng, Yujiao
  |  
收藏
  |  
浏览/下载:34/0
  |  
提交时间:2018/12/29
multi-objective optimization
POWER
environmental impact
MODEL
integrated energy system
improved multi-objective particle swarm
iron and steel
Blade layers optimization of wind turbines using FAST and improved PSO Algorithm
期刊论文
OAI收割
RENEWABLE ENERGY, 2012, 卷号: 42, 页码: 227-233
作者:
Liao, C. C.
;
Zhao, X. L.
;
Xu, J. Z.
收藏
  |  
浏览/下载:30/0
  |  
提交时间:2015/11/03
Blade layers
Optimum design
Spar caps
Maximum blade tip deflection
Improved particle swarm optimization
Seismic scalar wave equation inversion based on an improved particle swarm optimization algorithm
期刊论文
OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2011, 卷号: 54, 期号: 11, 页码: 2951-2959
作者:
Zhu Tong
;
Li Xiao-Fan
;
Li Yi-Qiong
;
Zhang Mei-Gen
  |  
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2018/09/26
Improved particle swarm optimization
Scalar wave equation
Velocity inversion
Parallel
Seismic scalar wave equation inversion based on an improved particle swarm optimization algorithm
期刊论文
OAI收割
CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2011, 卷号: 54, 期号: 11, 页码: 2951-2959
作者:
Zhu Tong
;
Li Xiao-Fan
;
Li Yi-Qiong
;
Zhang Mei-Gen
  |  
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2018/09/26
Improved particle swarm optimization
Scalar wave equation
Velocity inversion
Parallel
An improved discrete particle swarm optimization algorithm for TSP (EI CONFERENCE)
会议论文
OAI收割
2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007, November 2, 2007 - November 5, 2007, Silicon Valley, CA, United states
作者:
Wang Y.
;
Wang Y.
;
Wang Y.
;
Wang Y.
;
Wang Y.
收藏
  |  
浏览/下载:25/0
  |  
提交时间:2013/03/25
An Improved discrete particle swarm optimization (DPSO)-based algorithm for the traveling salesman problem (TSP) is proposed. In order to overcome the problem of premature convergence
a novel depressor is proposed and a diversity measure to control the swarm is also introduced which can be used to switch between the attractor and depressor. The proposed algorithm has been applied to a set of benchmark problems and compared with the existing algorithms for solving TSP using swarm intelligence. The results show that it can prevent premature convergence to a high degree
but still keeps a rapid convergence like the basic DPSO. 2007 IEEE.