10 citations · 24 across the 13 of their papers we have counts for
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NomadBBO: A direct-search framework for constrained mixed-variable blackbox optimization in Python
Edward Hallé-Hannan, Christophe Tribes
Mixed-variable blackbox optimization arises in simulation-based engineering and machine learning, where objective and constraint functions are expensive to evaluate and variables m…
Parallel versions of the mesh adaptive direct search algorithm
Sébastien Le Digabel, Antoine Lesage-Landry, Samuel Mendoza +1
This work surveys the different parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization. These problems can inherently imply hig…
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…
Surrogate-based categorical neighborhoods for mixed-variable blackbox optimization
Charles Audet, Youssef Diouane, Edward Hallé-Hannan +2
In simulation-based engineering, design choices are often obtained following the optimization of complex blackbox models. These models frequently involve mixed-variable domains wit…
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…
Efficient search strategies for constrained multiobjective blackbox optimization
Sébastien Le Digabel, Antoine Lesage-Landry, Ludovic Salomon +1
Multiobjective blackbox optimization deals with problems where the objective and constraint functions are the outputs of a numerical simulation. In this context, no derivatives are…
solar: A solar thermal power plant simulator for blackbox optimization benchmarking
Nicolau Andrés-Thió, Charles Audet, Miguel Diago +5
This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a c…
NOMAD version 4: Nonlinear optimization with the MADS algorithm
Charles Audet, Sébastien Le Digabel, Viviane Rochon Montplaisir +1
NOMAD is software for optimizing blackbox problems. In continuous development since 2001, it constantly evolved with the integration of new algorithmic features published in scient…