Mesh Adaptive Direct Search

2017 ◽  
pp. 135-156
Author(s):  
Charles Audet ◽  
Warren Hare
Keyword(s):  
Direct Search ◽  
Mesh Adaptive Direct Search

Robust optimization of noisy blackbox problems using the Mesh Adaptive Direct Search algorithm

2018 ◽  
Vol 12 (4) ◽  
pp. 675-689 ◽  
Author(s):  
Charles Audet ◽  
Amina Ihaddadene ◽  
Sébastien Le Digabel ◽  
Christophe Tribes
Keyword(s):  
Robust Optimization ◽  
Search Algorithm ◽  
Direct Search ◽  
Mesh Adaptive Direct Search ◽  
Direct Search Algorithm

Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints

2019 ◽  
Vol 31 (4) ◽  
pp. 689-702 ◽  
Author(s):  
Juliane Müller ◽  
Marcus Day
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Black Box ◽  
Direct Search ◽  
Test Problems ◽  
Large Set ◽  
Mesh Adaptive Direct Search ◽  
Surrogate Optimization ◽  
Computationally Expensive ◽  
Hidden Constraints

We introduce the algorithm SHEBO (surrogate optimization of problems with hidden constraints and expensive black-box objectives), an efficient optimization algorithm that employs surrogate models to solve computationally expensive black-box simulation optimization problems that have hidden constraints. Hidden constraints are encountered when the objective function evaluation does not return a value for a parameter vector. These constraints are often encountered in optimization problems in which the objective function is computed by a black-box simulation code. SHEBO uses a combination of local and global search strategies together with an evaluability prediction function and a dynamically adjusted evaluability threshold to iteratively select new sample points. We compare the performance of our algorithm with that of the mesh-based algorithms mesh adaptive direct search (MADS, NOMAD [nonlinear optimization by mesh adaptive direct search] implementation) and implicit filtering and SNOBFIT (stable noisy optimization by branch and fit), which assigns artificial function values to points that violate the hidden constraints. Our numerical experiments for a large set of test problems with 2–30 dimensions and a 31-dimensional real-world application problem arising in combustion simulation show that SHEBO is an efficient solver that outperforms the other methods for many test problems.

Metamaterial Design by Mesh Adaptive Direct Search

2013 ◽  
pp. 71-96
Author(s):  
Charles Audet ◽  
Kenneth Diest ◽  
Sébastien Le Digabel ◽  
Luke A. Sweatlock ◽  
Daniel E. Marthaler
Keyword(s):  
Direct Search ◽  
Mesh Adaptive Direct Search

Optimization of fastener pattern in airframe assembly

2020 ◽  
Vol 40 (5) ◽  
pp. 723-733
Author(s):  
Sergey Lupuleac ◽  
Tatiana Pogarskaia ◽  
Maria Churilova ◽  
Michael Kokkolaras ◽  
Elodie Bonhomme
Keyword(s):  
Optimization Problem ◽  
Computational Effort ◽  
Direct Search ◽  
Optimization Strategy ◽  
Mesh Adaptive Direct Search ◽  
Optimization Analysis ◽  
Content Type ◽  
Rear Wing ◽  
Random Character ◽  
Problem Formulations

Purpose The authors consider the problem of optimizing temporary fastener patterns in aircraft assembly. Minimizing the number of fasteners while maintaining final product quality is one of the key enablers for intensifying production in the aerospace industry. The purpose of this study is to formulate the fastener pattern optimization problem and compare different solving approaches on both test benchmarks and rear wing-to-fuselage assembly of an Airbus A350-900. Design/methodology/approach The first considered algorithm is based on a local exhaustive search. It is proved to be efficient and reliable but requires much computational effort. Secondly, the Mesh Adaptive Direct Search (MADS) implemented in NOMAD software (Nonlinear Optimization by Mesh Adaptive Direct Search) is used to apply the powerful mathematical machinery of surrogate modeling and associated optimization strategy. In addition, another popular optimization algorithm called simulated annealing (SA) was implemented. Since a single fastener pattern must be used for the entire aircraft series, cross-validation of obtained results was applied. The available measured initial gaps from 340 different aircraft of the A350-900 series were used. Findings The results indicated that SA cannot be applicable as its random character does not provide repeatable results and requires tens of runs for any optimization analysis. Both local variations (LV) method and MADS have proved to be appropriate as they improved the existing fastener pattern for all available gaps. The modification of the MADS' search step was performed to exploit all the information the authors have about the problem. Originality/value The paper presents deterministic and probabilistic optimization problem formulations and considers three different approaches for their solution. The existing fastener pattern was improved.

Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization

2015 ◽  
Vol 17 (2) ◽  
pp. 333-358 ◽  
Author(s):  
Charles Audet ◽  
Sébastien Le Digabel ◽  
Christophe Tribes
Keyword(s):  
Search Algorithm ◽  
Direct Search ◽  
Dynamic Scaling ◽  
Mesh Adaptive Direct Search ◽  
Blackbox Optimization ◽  
Direct Search Algorithm
Sign in / Sign up

Export Citation Format

Share Document