Comparative study between the generalized reduced gradient and genetic algorithm in multiple response optimization
DOI:
https://doi.org/10.14488/1676-1901.v17i2.2566Keywords:
Optimization. Multiple Response. Genetic Algorithm. GRG.Abstract
In this paper we present a comparative study between the Generalized Reduced Gradient (GRG) and Genetic Algorithm (GA) methods to optimize multiple-response processes. Results from experiment design were used to compose the objective function to be minimized. The case studies in this work were selected from literature. A Microsoft Excel spreadsheet was used for parameters optimization using GRG, and the Scilab software was used to GA. Ten replicates were performed and the mean of the results was obtained. To assess the methods was used performance measures based on the mean percentage error. From the performance measures used, the AG showed better results compared to the GRG, indicating that the AG can generate better responses than GRG.
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