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Adaptive direct search algorithms with relaxable and quantifiable constraints
Charles Audet, Théo Denorme, Youssef Diouane +2
This work introduces ADS-PB, an extension of the Adaptive Direct Search (ADS) framework for solving constrained blackbox optimization problems. With ADS, iterates progress without…
Efficient multidisciplinary design via Bayesian optimization
Nathalie Bartoli, Thierry Lefebvre, Rémi Lafage +3
This study introduces SEGOMOE, a Bayesian optimization tool for optimizing complex, computationally expensive systems, especially in aeronautics. It efficiently handles mixed desig…
A penalty-interior point method combined with MADS for equality and inequality constrained optimization
Charles Audet, Andrea Brilli, Youssef Diouane +3
This work introduces MADS-PIP, an efficient framework that integrates a penalty-interior point strategy into the mesh adaptive direct search (MADS) algorithm for solving nonsmooth…
Adaptive direct search algorithms for constrained optimization
Charles Audet, Théo Denorme, Youssef Diouane +2
Two families of directional direct search methods have emerged in derivative-free and blackbox optimization (DFO and BBO), each based on distinct principles: Mesh Adaptive Direct S…
CatMADS: Mesh Adaptive Direct Search for constrained blackbox optimization with categorical variables
Charles Audet, Youssef Diouane, Edward Hallé-Hannan +2
Solving optimization problems in which functions are blackboxes and variables involve different types poses significant theoretical and algorithmic challenges. Nevertheless, such s…
High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings
Rémy Priem, Youssef Diouane, Nathalie Bartoli +2
Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventiona…