Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization Algorithms

This paper uses an augmented Lagrangian method based on an inexact exponential penalty function to solve constrained multiobjective optimization problems. Two algorithms have been proposed in this study. The first algorithm uses a projected gradient, while the second uses the steepest descent method...

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Main Authors: Appolinaire Tougma, Kounhinir Some
Format: Article
Language:English
Published: Hindawi Limited 2024-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2024/9615743
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author Appolinaire Tougma
Kounhinir Some
author_facet Appolinaire Tougma
Kounhinir Some
author_sort Appolinaire Tougma
collection DOAJ
description This paper uses an augmented Lagrangian method based on an inexact exponential penalty function to solve constrained multiobjective optimization problems. Two algorithms have been proposed in this study. The first algorithm uses a projected gradient, while the second uses the steepest descent method. By these algorithms, we have been able to generate a set of nondominated points that approximate the Pareto optimal solutions of the initial problem. Some proofs of theoretical convergence are also proposed for two different criteria for the set of generated stationary Pareto points. In addition, we compared our method with the NSGA-II and augmented the Lagrangian cone method on some test problems from the literature. A numerical analysis of the obtained solutions indicates that our method is competitive with regard to the test problems used for the comparison.
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spelling doaj.art-c118983d985c4c3a8205be232010f36a2024-01-18T00:00:02ZengHindawi LimitedJournal of Applied Mathematics1687-00422024-01-01202410.1155/2024/9615743Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization AlgorithmsAppolinaire Tougma0Kounhinir Some1Department of MathematicsDepartment of MathematicsThis paper uses an augmented Lagrangian method based on an inexact exponential penalty function to solve constrained multiobjective optimization problems. Two algorithms have been proposed in this study. The first algorithm uses a projected gradient, while the second uses the steepest descent method. By these algorithms, we have been able to generate a set of nondominated points that approximate the Pareto optimal solutions of the initial problem. Some proofs of theoretical convergence are also proposed for two different criteria for the set of generated stationary Pareto points. In addition, we compared our method with the NSGA-II and augmented the Lagrangian cone method on some test problems from the literature. A numerical analysis of the obtained solutions indicates that our method is competitive with regard to the test problems used for the comparison.http://dx.doi.org/10.1155/2024/9615743
spellingShingle Appolinaire Tougma
Kounhinir Some
Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization Algorithms
Journal of Applied Mathematics
title Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization Algorithms
title_full Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization Algorithms
title_fullStr Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization Algorithms
title_full_unstemmed Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization Algorithms
title_short Inexact Exponential Penalty Function with the Augmented Lagrangian for Multiobjective Optimization Algorithms
title_sort inexact exponential penalty function with the augmented lagrangian for multiobjective optimization algorithms
url http://dx.doi.org/10.1155/2024/9615743
work_keys_str_mv AT appolinairetougma inexactexponentialpenaltyfunctionwiththeaugmentedlagrangianformultiobjectiveoptimizationalgorithms
AT kounhinirsome inexactexponentialpenaltyfunctionwiththeaugmentedlagrangianformultiobjectiveoptimizationalgorithms