SORB
发表于 2025-3-21 18:03:27
书目名称Intelligent Computing Theories and Application影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0469476<br><br> <br><br>书目名称Intelligent Computing Theories and Application读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0469476<br><br> <br><br>
Folklore
发表于 2025-3-21 21:10:43
An Efficient Competitive Swarm Optimizer for Solving Large-Scale Multi-objective Optimization Proble strong exploration ability, denoted as E-CSO, is presented in this paper, which designs a novel three-particle-based particle updating strategy to improve the search efficiency. The experimental results validate the high efficiency and effectiveness of our proposed approach when solving various LSMOPs.
Mitigate
发表于 2025-3-22 01:20:56
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sinoatrial-node
发表于 2025-3-22 06:06:05
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outrage
发表于 2025-3-22 09:21:11
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可互换
发表于 2025-3-22 15:22:55
Hyperspectral Image Classification with Locally Linear Embedding, 2D Spatial Filtering, and SVMh spatial information and spectral information are taken into consideration for HSI classification. Experiments demonstrate that this new algorithm is extremely competitive when compared to several existing methods.
Corroborate
发表于 2025-3-22 18:52:00
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2否定
发表于 2025-3-23 00:33:38
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poliosis
发表于 2025-3-23 03:07:27
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fulmination
发表于 2025-3-23 06:11:31
An Improved Teaching-Learning-Based Optimization for Multitask Optimization Problemsed into the teaching stage which allow students to receive a wide range of knowledge and find individuals with great differences. MTTLBO is compared with some advanced evolutionary multitask algorithms. The experimental results state clearly that the MTTLBO algorithm owns excellent performance on nine sets of single-objective problems.