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Artificial Bee Colony Algorithm Based on K-Means Clustering for Droplet Property Optimization
Sun LL(孙丽玲); Hu JT(胡静涛); He MW(何茂伟); Chen HN(陈瀚宁)
作者部门信息服务与智能控制技术研究室
会议名称8th International Conference on Intelligent Robotics and Applications (ICIRA 2015)
会议日期August 24-27, 2015
会议地点Portsmouth, UK
会议录名称Lecture Notes in Computer Science
出版者Springer Verlag
出版地Berlin
2015
页码34-44
收录类别EI ; CPCI(ISTP)
EI收录号20163702786427
WOS记录号WOS:000364714000004
产权排序1
ISSN号0302-9743
ISBN号978-3-319-22878-5
关键词Piezoelectric Inkjet System Lumped Element Modeling Artificial Bee Colony Algorithm Swarm Intelligent Algorithm
摘要The major challenge in printable electronics fabrication is to effectively and accurately control a drop-on-demand (DoD) inkjet printhead for high printing quality. In this paper, a prediction model based on Lumped Element Modeling (LEM) is proposed to search the parameters of driving waveform for obtaining the desired droplet properties. Although the evolution algorithms are helpful to solve this problem, the classical evolution algorithms may get trapped into local optimal due to the inefficiency of local search. To overcome it, we present an improved artificial bee colony algorithm based on K-means clustering (KCABC), which enhances the population diversity by dynamically clustering and increases the convergence rates by the modification of information communication in the employed bees’ phase. Combined with KCABC, the prediction model is applied to optimize the droplet volume and velocity of nano-silver ink for high printing quality. Experimental results demonstrate the proposed prediction model with KCABC plays a good performance in terms of efficiency and accuracy of searching the appropriate combination of waveform parameters for printable electronics fabrication.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/17378
专题信息服务与智能控制技术研究室
作者单位1.Department of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.University of Chinese Academy of Sciences, Beijing, 100040, China
3.School of Computer Science and Software, Tianjin Polytechnic University, Tianjin, 300387, China
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GB/T 7714
Sun LL,Hu JT,He MW,et al. Artificial Bee Colony Algorithm Based on K-Means Clustering for Droplet Property Optimization[C]. Berlin:Springer Verlag,2015:34-44.
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