Application of uniform design for mixture experiments in multi-objective optimization

When the number of experimental points and variables in uniform design for mixture experiments is too large, the requirements of uniformity and calculation efficiency are hard to be satisfied simultaneously. In this paper based on the transformation of U-type matrix method, the uniformity is improve...

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Bibliographic Details
Published in:2014 IEEE International Conference on Progress in Informatics and Computing pp. 350 - 354
Main Authors: Zhailiu Hao, Zuyuan Liu, Baiwei Feng
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2014
Subjects:
Algorithm design and analysis
Educational institutions
genetic algorithm
Genetic algorithms
multi-objective optimization
Optimization
Pareto front
physical programming
Programming profession
Search problems
uniform design for mixture experiments
ISBN:9781479920334, 1479920339
Online Access:Get full text
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Summary:When the number of experimental points and variables in uniform design for mixture experiments is too large, the requirements of uniformity and calculation efficiency are hard to be satisfied simultaneously. In this paper based on the transformation of U-type matrix method, the uniformity is improved by cutting method, and the calculation efficiency problem is solved by genetic algorithm. So the uniform design for mixture experiments with good uniformity and arbitrary number of experimental points and variables is able to be generated. Then it is applied to the multi-objective optimization algorithm based on physical programming to improve optimization quality and generate evenly distributed Pareto front. Finally, the effectiveness of the improved uniform design for mixture experiments in multi-objective optimization is verified by a numerical example with three objectives.
ISBN:9781479920334
1479920339
DOI:10.1109/PIC.2014.6972356