Open AccessNew Approaches in Simulation-driven Optimization
Author(s) -
Yoel Tenne
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1670/1/012010
Subject(s) - convergence (economics) , discriminant , linear discriminant analysis , computer science , machine learning , mathematical optimization , artificial intelligence , work (physics) , algorithm , mathematics , engineering , mechanical engineering , economics , economic growth
This paper presents a work regarding the integration of discriminant functions (classifiers) with search algorithms to tackle the problem of failed simulation runs. The discriminant output is used to guide the search towards better solutions while minimizing adverse effects. The search is managed with a trust-region approach for convergence in the presence of prediction inaccuracies. Numerical evaluations based on engineering problem show that the approach yielded better final results in the mean and median statistics when compared to reference algorithms.