Multi-Objective Optimization by Gaussian Genetic Algorithm and its Application in Injection Modeling

Abstract:

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.

• Periodical: Advanced Materials Research (Volumes 399-401)
• Pages: 1672-1676
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.399-401.1672
• Online since: November 2011
• Authors: Xiao Ping Liao, Ting Ruan, Wei Xia, Jun Yan Ma, Liu Lin Li
• Keywords: Gaussian Genetic Algorithms, Gaussian Mutation, Gaussian Process Surrogate Model, Injection Molding

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