Multi-Objective Optimization of Blast Simulation Using Surrogate Model
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.
Multi-objective, Optimization, Genetic algorithm, Surrogate, Blast, Simulations