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,

• author = "Chih-Ming Hsu",
• title = "Improving the lighting performance of a 3535 packaged hi-power LED using genetic programming, quality loss functions and particle swarm optimization",
• journal = "Applied Soft Computing",
• volume = "12",
• number = "9",
• pages = "2933--2947",
• year = "2012",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2012.04.023",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494612002165",
• keywords = "genetic algorithms, genetic programming, Light-emitting diode, Lighting performance, Taguchi quality loss functions, Particle swarm optimization, Multi-response parameter design",
• 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

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