research-article

Constrained blackbox optimization with the NOMAD solver on the COCO constrained test suite

Authors:

Sébastien Le Digabel,

Ludovic Salomon,

Christophe TribesAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1683 - 1690

https://doi.org/10.1145/3520304.3534019

Published: 19 July 2022 Publication History

Abstract

The context of this work is constrained blackbox optimization. It describes the mesh adaptive direct search (MADS) derivative-free optimization algorithm using the progressive barrier strategy to handle quantifiable and relaxable constraints. Through its implementation in the NOMAD solver, MADS is tested on the new bbob-constrained suite of analytical constrained problems from the COCO platform, and compared with the CMA-ES heuristic. Computational tests are illustrated with the postprocessing graphs from the COCO platform, as well as with data profiles, an established tool in the derivative-free optimization community, adapted here for the constrained case. The results illustrate that researchers must be very careful with the use of these tools, which are complementary and should ideally be used together. Their many variations may show different outcomes, and hence many graphs are necessary in order to provide the best overall view.

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Cited By

Khan NSulaiman MAlshammari F(2022)Analysis of heat transmission in convective, radiative and moving rod with thermal conductivity using meta-heuristic-driven soft computing techniqueStructural and Multidisciplinary Optimization10.1007/s00158-022-03414-765:11Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00158-022-03414-7

Index Terms

Constrained blackbox optimization with the NOMAD solver on the COCO constrained test suite
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Nonconvex optimization
  2. Mathematical software
    1. Mathematical software performance
    2. Solvers

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cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Publication History

Published: 19 July 2022

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NSERC

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Khan NSulaiman MAlshammari F(2022)Analysis of heat transmission in convective, radiative and moving rod with thermal conductivity using meta-heuristic-driven soft computing techniqueStructural and Multidisciplinary Optimization10.1007/s00158-022-03414-765:11Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00158-022-03414-7

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