Journal Article10.3795/KSME-A.2014.38.5.489
Development of Optimization Algorithm Using Sequential Design of Experiments and Micro-Genetic Algorithm
Jung-Hwan Lee,Myung Won Suh +1 more
TL;DR: This study proposes an optimization algorithm based on the sequential design of experiments (SDOE) and an MGA that can be used to reduce trial-and-error in the conventional approximate optimization method.
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Abstract: A micro-genetic algorithm (MGA) is one of the improved forms of a genetic algorithm. It is used to reduce the number of iterations and the computing resources required by using small populations. The efficiency of MGAs has been proved through many problems, especially problems with 3-5 design variables. This study proposes an optimization algorithm based on the sequential design of experiments (SDOE) and an MGA. In a previous study, the authors used the SDOE technique to reduce trial-and-error in the conventional approximate optimization method by using the statistical design of experiments (DOE) and response surface method (RSM) systematically. The proposed algorithm has been applied to various mathematical examples and a structural problem.
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TL;DR: In this article, the authors used the table of orthogonal arrays to simulate the roll forming process of a slide rail and a cost function was obtained from the results by using the design of experiments such as the response surface method.
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