基于镜头对立学习和自适应β爬坡全局优化的改进大猩猩部队优化器

IF 2.2 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY
Yaning Xiao, Xue Sun, Yanling Guo, Sanping Li, Yapeng Zhang, Yangwei Wang
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引用次数: 20

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An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization
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来源期刊
Cmes-computer Modeling in Engineering & Sciences
Cmes-computer Modeling in Engineering & Sciences ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
3.80
自引率
16.70%
发文量
298
审稿时长
7.8 months
期刊介绍: This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro), and various time scales ( picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.
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