Multi-Objective Optimization by Gaussian Genetic Algorithm and its Application in Injection Modeling
2011 ◽
Vol 399-401
◽
pp. 1672-1676
Keyword(s):
A method of combining Gaussian Process (GP) Surrogate model and Gaussian genetic algorithm is discussed to optimize the injection molding process. GP surrogate model is constructed to map the complex non-linear relationship between process conditions and quality indexes of the injection molding parts. While the surrogate model is established, a Gaussian genetic algorithm (GGA) combined with Gaussian mutation and hybrid genetic algorithm is employed to evaluate the model to search the global optimal solutions. The example presented shows that the GGA is more effective for the process optimization of injection molding.
2012 ◽
Vol 463-464
◽
pp. 587-591
◽
2011 ◽
Vol 58
(5-8)
◽
pp. 521-531
◽
2011 ◽
Vol 32
(6)
◽
pp. 3457-3464
◽
2015 ◽
Vol 78
(9-12)
◽
pp. 1813-1826
◽
2005 ◽
Vol 11
(3)
◽
pp. 167-173
◽
2014 ◽
Vol 16
(16)
◽
pp. 1-12
2016 ◽
Vol 17
(10)
◽
pp. 499-508
◽
1982 ◽
Vol 22
(9)
◽
pp. 560-568
◽
Keyword(s):