中国科学院机构知识库网格系统: 检索

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	Rapid detection of protein content in rice based on Raman and near-infrared spectroscopy fusion strategy combined with characteristic wavelength selection 期刊论文 OAI收割 INFRARED PHYSICS & TECHNOLOGY, 2023, 卷号: 129, 页码: 8 作者: Wang, Zhiqiang; Liu, Jinming; Zeng, Changhao; Bao, Changhao; Li, Zhijiang \| 收藏 \| 浏览/下载：1/0 \| 提交时间：2025/11/14 Protein content Raman spectroscopy Near -infrared spectroscopy Data fusion Partial least squares Improved binary particle swarm optimization algorithm
	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：46/0 \| 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：34/0 \| 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 \| 收藏 \| 浏览/下载：40/0 \| 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：38/0 \| 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：47/0 \| 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 \| 收藏 \| 浏览/下载：29/0 \| 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：20/0 \| 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：39/0 \| 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.