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Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems 期刊论文  OAI收割
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 8, 页码: 1786-1801
作者:  
Sheng Qi;  Rui Wang;  Tao Zhang;  Weixiong Huang;  Fan Yu
  |  收藏  |  浏览/下载:23/0  |  提交时间:2024/07/16
A Two-layer Encoding Learning Swarm Optimizer based on Frequent Itemsets for Sparse Large-scale Multi-objective Optimization 期刊论文  OAI收割
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 6, 页码: 1342-1357
作者:  
Sheng Qi;  Rui Wang;  Tao Zhang;  Xu Yang;  Ruiqing Sun
  |  收藏  |  浏览/下载:7/0  |  提交时间:2024/05/22
Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization 期刊论文  OAI收割
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 6050-6065
作者:  
Wang, Bo;  Yuan, Chunfeng;  Li, Bing;  Ding, Xinmiao;  Li, Zeya
  |  收藏  |  浏览/下载:27/0  |  提交时间:2021/08/15
Multi-scale asymptotic analysis and computation of the elliptic eigenvalue problems in curvilinear coordinates 期刊论文  OAI收割
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2018, 卷号: 340, 页码: 340-365
作者:  
Ma, Qiang;  Li, Zhihui;  Cui, Junzhi
  |  收藏  |  浏览/下载:35/0  |  提交时间:2018/10/07
Chinese-German Computational and Applied Mathematics 期刊论文  OAI收割
COMPUTATIONAL METHODS IN APPLIED MATHEMATICS, 2016, 卷号: 16, 期号: 4, 页码: 605-608
作者:  
Hoppe, Ronald H. W.;  Hu, Jun;  Peter, Malte A.;  Rannacher, Rolf;  Shi, Zhongci
  |  收藏  |  浏览/下载:24/0  |  提交时间:2018/07/30
Optimizing control mode of optical payloads based on multi-population genetic algorithm (EI CONFERENCE) 会议论文  OAI收割
2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, August 9, 2009 - August 12, 2009, Changchun, China
Xu W.; Jin G.
收藏  |  浏览/下载:24/0  |  提交时间:2013/03/25
Optimizing the control mode of optical payload could improve the payload's work efficiency. When the mathematic model of payload's control mode was established  optimization problems could be boiled down to seek the maximal value of the function with many variables. This paper put forward a calculational method to base the mathematic model  the calculational method was multi-population genetic algorithm (MPGA). After the 0-1 variables and real multidimensional ones had been coded separately  the algorithm established the populations independently  made the multidimensional variables as the centrosome and combined 0-1 ones to work out the maximal value of the function which was established to describe the control mode of the optical payload. Moreover  the emulational experiment had been done with the material arithmetic operators. The result indicates that the method using MPGA can hurdle the disadvantage of traditional ones which calculate slowly and get into local best value trap easily . It has the characters that not only fits large scale area scout  but also gets the best value in full scale rapidly  so this algorithm can better satisfy the technology demand for the intelligent control of optical payload. 2009 IEEE.