Applying genetic programming and ant colony optimisation to improve the geometric design of a reflector

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{journals/ijsysc/Hsu12,

• author = "Chih-Ming Hsu",
• title = "Applying genetic programming and ant colony optimisation to improve the geometric design of a reflector",
• journal = "International Journal of Systems Science",
• year = "2012",
• number = "5",
• volume = "43",
• pages = "972--986",
• month = may,
• keywords = "genetic algorithms, genetic programming, light-emitting diode, reflector, ant colony optimisation, multi-response parameter design",
• ISSN = "0020-7721",
• DOI = "doi:10.1080/00207721.2010.547627",
• size = "15 pages",
• abstract = "The lighting performance of an LED (light-emitting diode) flash is significantly influenced by the geometric form of a reflector. Previously, design engineers have usually determined the geometric design of a reflector according to the principles of optics and their own experience. Some real reflectors have then been created to verify the feasibility and performance of a certain geometric design. This, however, is a costly and time-consuming procedure. Furthermore, the geometric design of a reflector cannot be proved to be actually optimal. This study proposes a systematic approach based on genetic programming (GP) and ant colony optimisation (ACO), called the GP-ACO procedure, to improve the geometric design of a reflector. A case study is used to demonstrate the feasibility and effectiveness of the proposed optimisation procedure. The results show that all the crucial quality characteristics of an LED flash fulfil the required specifications; thus, the optimal geometric parameter settings of the reflector obtained can be directly applied to mass production. Consequently, the proposed GP-ACO procedure can be considered an effective method for resolving general multi-response parameter design problems",
• notes = "Department of Business Administration, Minghsin University of Science and Technology, 1 Hsin Hsin Road, Hsin Feng, Hsin Chu, 30401 Taiwan",
• bibdate = "2012-03-05",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijsysc/ijsysc43.html#Hsu12",

}

Genetic Programming entries for Chih-Ming Hsu

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