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Titlebook: Computational Intelligence and Intelligent Systems; 9th International Sy Kangshun Li,Wei Li,Yong Liu Conference proceedings 2018 Springer N

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書目名稱Computational Intelligence and Intelligent Systems
副標(biāo)題9th International Sy
編輯Kangshun Li,Wei Li,Yong Liu
視頻videohttp://file.papertrans.cn/233/232427/232427.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Computational Intelligence and Intelligent Systems; 9th International Sy Kangshun Li,Wei Li,Yong Liu Conference proceedings 2018 Springer N
描述This two-volume set (CCIS 873 and CCIS 874) constitutes the thoroughly refereed proceedings of the 9th International Symposium, ISICA 2017, held in Guangzhou, China, in November 2017.?The 101 full papers presented in both volumes were carefully reviewed and selected from 181 submissions. This second volume is organized in topical sections on?swarm intelligence:?cooperative Search,?swarm optimization;?complex systems modeling:?system dynamic,?multimedia simulation;?intelligent information systems:?information retrieval,?e-commerce platforms;?artificial intelligence and robotics:?query optimization,?intelligent engineering;?virtualization:?motion-based tracking,?image recognition..
出版日期Conference proceedings 2018
關(guān)鍵詞artificial intelligence; association rules; cloud computing; clustering algorithms; data mining; differen
版次1
doihttps://doi.org/10.1007/978-981-13-1651-7
isbn_softcover978-981-13-1650-0
isbn_ebook978-981-13-1651-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2018
The information of publication is updating

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IT? Algorithm with Cooperative Coevolution for Large Scale Global Optimizationcarried out extensive computational studies on the CEC’2010 benchmark function. Experimental results show that our algorithm achieves competitive results compared with other four state-of-the-art algorithms in the large scale global optimization problems.
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Hybrid Colliding Bodies Optimization for Solving Emergency Materials Transshipment Model with Time W algorithm (GA) imbedding with linear programming algorithm is proposed to solve the problem through analyzing the trait of the model. The hybrid algorithm improves the performance of CBO algorithm in the discrete field. Experimental results demonstrate the efficiency of the model and algorithm.
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