Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES

Paul Dufossé; Nikolaus Hansen

doi:10.1145/3449639.3459340

Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES

Paul Dufossé
, Nikolaus Hansen

INRIA Institut National de Recherche en Informatique et en Automatique

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

In this paper, we investigate a non-elitist Evolution Strategy designed to handle black-box constraints by an adaptive Augmented Lagrangian penalty approach, AL-(μ/μw, λ)-CMA-ES, on problems with up to 28 constraints. Based on stability and performance observations, we propose an improved default parameter setting. We exhibit failure cases of the Augmented Lagrangian technique and show how surrogate modeling of the constraints can overcome some difficulties. Several variants of AL-CMA-ES are compared on a set of nonlinear constrained problems from the literature. Simple adaptive penalty techniques serve as a baseline for comparison.

langue originale	Anglais
titre	GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference
Editeur	Association for Computing Machinery, Inc
Pages	519-527
Nombre de pages	9
ISBN (Electronique)	9781450383509
Les DOIs	https://doi.org/10.1145/3449639.3459340
état	Publié - 26 juin 2021
Evénement	2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France Durée: 10 juil. 2021 → 14 juil. 2021

Série de publications

Nom	GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference

Une conférence

Une conférence	2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Pays/Territoire	France
La ville	Virtual, Online
période	10/07/21 → 14/07/21

Accès au document

10.1145/3449639.3459340

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Dufossé, P., & Hansen, N. (2021). Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES. Dans GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (p. 519-527). (GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3449639.3459340

Dufossé, Paul ; Hansen, Nikolaus. / Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES. GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2021. p. 519-527 (GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference).

@inproceedings{321c099eb9a648658cf4e52828d77356,

title = "Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES",

abstract = "In this paper, we investigate a non-elitist Evolution Strategy designed to handle black-box constraints by an adaptive Augmented Lagrangian penalty approach, AL-(μ/μw, λ)-CMA-ES, on problems with up to 28 constraints. Based on stability and performance observations, we propose an improved default parameter setting. We exhibit failure cases of the Augmented Lagrangian technique and show how surrogate modeling of the constraints can overcome some difficulties. Several variants of AL-CMA-ES are compared on a set of nonlinear constrained problems from the literature. Simple adaptive penalty techniques serve as a baseline for comparison.",

keywords = "Augmented lagrangian, CMA-ES, Constrained optimization, Evolution strategies, Penalty techniques",

author = "Paul Dufoss{\'e} and Nikolaus Hansen",

note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 ; Conference date: 10-07-2021 Through 14-07-2021",

year = "2021",

month = jun,

day = "26",

doi = "10.1145/3449639.3459340",

language = "English",

series = "GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery, Inc",

pages = "519--527",

booktitle = "GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference",

}

Dufossé, P & Hansen, N 2021, Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES. Dans GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference. GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, p. 519-527, 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Virtual, Online, France, 10/07/21. https://doi.org/10.1145/3449639.3459340

Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES. / Dufossé, Paul; Hansen, Nikolaus.
GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, 2021. p. 519-527 (GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES

AU - Dufossé, Paul

AU - Hansen, Nikolaus

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N2 - In this paper, we investigate a non-elitist Evolution Strategy designed to handle black-box constraints by an adaptive Augmented Lagrangian penalty approach, AL-(μ/μw, λ)-CMA-ES, on problems with up to 28 constraints. Based on stability and performance observations, we propose an improved default parameter setting. We exhibit failure cases of the Augmented Lagrangian technique and show how surrogate modeling of the constraints can overcome some difficulties. Several variants of AL-CMA-ES are compared on a set of nonlinear constrained problems from the literature. Simple adaptive penalty techniques serve as a baseline for comparison.

AB - In this paper, we investigate a non-elitist Evolution Strategy designed to handle black-box constraints by an adaptive Augmented Lagrangian penalty approach, AL-(μ/μw, λ)-CMA-ES, on problems with up to 28 constraints. Based on stability and performance observations, we propose an improved default parameter setting. We exhibit failure cases of the Augmented Lagrangian technique and show how surrogate modeling of the constraints can overcome some difficulties. Several variants of AL-CMA-ES are compared on a set of nonlinear constrained problems from the literature. Simple adaptive penalty techniques serve as a baseline for comparison.

KW - Augmented lagrangian

KW - CMA-ES

KW - Constrained optimization

KW - Evolution strategies

KW - Penalty techniques

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DO - 10.1145/3449639.3459340

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T3 - GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference

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PB - Association for Computing Machinery, Inc

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Dufossé P, Hansen N. Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES. Dans GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc. 2021. p. 519-527. (GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference). doi: 10.1145/3449639.3459340

Augmented lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES

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