MOOMIE article data : Efficient multi-objective optimization methodology for industrial engineering models with uncertain parameters
收藏DataCite Commons2023-05-04 更新2024-08-18 收录
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https://figshare.com/articles/dataset/MOOMIE_article_submitted_to_Engineering_Optimisation_journal_Efficient_multi-objective_optimization_methodology_for_industrial_engineering_models_with_uncertain_parameters/22756799
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资源简介：
For each problem (mechanical or modified B&K) we have one folder per MOO problem solution. Inside, 4 files exist : 1. hist_opt_obj1.txt - history of all results calculated during DOE and each mono-objective optimization (used to create the approximation model for the first NSGA-II opptimization) 2.opt_f2-f2_limits.txt - extremums of the Pareto front (results of the mono-objective optimization for each PF) 3.pareto_meta1 - temprorary output in Abq Insight from first optimisation on approximation RBF model, used in Abauqs Insight as an input for final NSGA-II 4.pareto_out_final - output from Abaqus Insight NSGA-II final solver (file used for parametrisation and Cij identification). <br>
提供机构：
figshare
创建时间：
2023-05-04
