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Titlebook: Swarm, Evolutionary, and Memetic Computing; Second International Bijaya Ketan Panigrahi,Ponnuthurai Nagaratnam Suga Conference proceedings

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发表于 2025-3-21 20:01:36 | 显示全部楼层 |阅读模式
书目名称Swarm, Evolutionary, and Memetic Computing
副标题Second International
编辑Bijaya Ketan Panigrahi,Ponnuthurai Nagaratnam Suga
视频videohttp://file.papertrans.cn/884/883687/883687.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Swarm, Evolutionary, and Memetic Computing; Second International Bijaya Ketan Panigrahi,Ponnuthurai Nagaratnam Suga Conference proceedings
描述These two volumes, LNCS 7076 and LNCS 7077, constitute the refereed proceedings of the Second International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2011, held in Visakhapatnam, India, in December 2011. The 124 revised full papers presented in both volumes were carefully reviewed and selected from 422 submissions. The papers explore new application areas, feature new bio-inspired algorithms for solving specific hard optimization problems, and review the latest progresses in the cutting-edge research with swarm, evolutionary, and memetic computing in both theoretical and practical aspects.
出版日期Conference proceedings 2011
关键词Web mining; metaheuristics strategy; multi-objective optimization; parallel genetic algorithms; recommen
版次1
doihttps://doi.org/10.1007/978-3-642-27172-4
isbn_softcover978-3-642-27171-7
isbn_ebook978-3-642-27172-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag GmbH Berlin Heidelberg 2011
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发表于 2025-3-21 22:31:33 | 显示全部楼层
A Quantized Invasive Weed Optimization Based Antenna Array Synthesis with Digital Phase Control,his paper the array factor is expressed mathematically by a linear transform, which is similar to Discrete Cosine Transform (DCT). This proposed algorithm is also found to be efficient in computing for large arrays.
发表于 2025-3-22 03:28:04 | 显示全部楼层
Comparative Performance Study of Genetic Algorithm and Particle Swarm Optimization Applied on Off-gent values of variants of the respective algorithms and a comparative study has been carried out based on number of iterations taken to find optimal solution, CPU utilization time and also quality of solutions. The comparative analysis shows that the Particle Swarm Optimization technique performs be
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Udit Halder,Dipankar Maity,Preetam Dasgupta,Swagatam Das
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Tirimula Rao Benala,Satchidananda Dehuri,G. S. Surya Vamsi Sirisetti,Aditya Pagadala
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M. Jagadeesh Kumar,Subranshu Sekhar Dash,M. Arun Bhaskar,C. Subramani,S. Vivek
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