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Multi-Objective Optimization of Blast Simulation Using Surrogate Model

Show simple item record Tsuga, Toshihiro
dc.creator Tsuga, Toshihiro 2007-12-12T16:14:50Z 2007-12-12T16:14:50Z 2007-12-12T16:14:50Z
dc.description.abstract A multi objective optimization approach using a Kriging model coupled with a Multi Objective Genetic Algorithm (MOGA) is applied to a blast damage maximization problem composed of two objectives, namely number of casualties and damage to buildings. The predicted Pareto front is located using a MOGA on the Kriging model. The location with maximum uncertainty along the Pareto front is added to the list of sample points. After each sampling, the Kriging model is reconstructed and this process is repeated until the maximum uncertainty is reduced. The cases run show that the Pareto front is not always intuitively discernable. `Best locations’ can vary significantly depending on the weight given to each optimization objective. The results also indicate that the effect of the additional cost incurred by the procedure to construct the `model of the model’ totally compensates the computational expense.
dc.language.iso en_US en
dc.subject multi-objective en_US
dc.subject optimization en_US
dc.subject genetic algorithm en_US
dc.subject surrogate en_US
dc.subject blast en_US
dc.subject simulations en_US
dc.title Multi-Objective Optimization of Blast Simulation Using Surrogate Model en
dc.type Thesis en Master of Science in Computational Sciences and Informatics en Master's en Computational Sciences and Informatics en George Mason University en

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