Improving the lighting performance of a 3535 packaged hi-power LED using genetic programming, quality loss functions and particle swarm optimization
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- @Article{Hsu20122933,
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author = "Chih-Ming Hsu",
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title = "Improving the lighting performance of a 3535 packaged
hi-power {LED} using genetic programming, quality loss
functions and particle swarm optimization",
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journal = "Applied Soft Computing",
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year = "2012",
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volume = "12",
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number = "9",
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pages = "2933--2947",
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keywords = "genetic algorithms, genetic programming,
Light-emitting diode, Lighting performance, Taguchi
quality loss functions, Particle swarm optimization,
PSO, Multi-response parameter design",
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ISSN = "1568-4946",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494612002165",
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DOI = "doi:10.1016/j.asoc.2012.04.023",
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size = "15 pages",
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abstract = "The lighting performance of a 3535 packaged hi-power
LED (light-emitting diode) is mainly influenced by its
geometric design and the refractive properties of its
materials. In the past, engineers often determined the
settings of the geometric parameters and selected the
refractive properties of the materials through a
trial-and-error process based on the principles of
optics and their own experience. This procedure was
costly and time-consuming, and its use did not ensure
that the settings of the design parameters were
optimal. Therefore, this study proposed a hybrid
approach based on genetic programming (GP), Taguchi
quality loss functions, and particle swarm optimisation
(PSO) to solve the multi-response parameter design
problems. The feasibility and effectiveness of the
proposed approach was demonstrated by a case study on
improving the lighting performance of an LED. The
confirmation results showed that all of the key quality
characteristics of an LED fulfil the required
specifications, and the comparison found that the
proposed hybrid approach outperforms the traditional
Taguchi method in solving this multi-response parameter
design problem. The proposed hybrid approach can be
extended to solve parameter design problems with
multiple responses in various application fields.",
- }
Genetic Programming entries for
Chih-Ming Hsu
Citations