A neurodynamic optimization approach to distributed nonconvex optimization based on an HP augmented Lagrangian function

This paper develops a neurodynamic model for distributed nonconvex-constrained optimization. In the distributed constrained optimization model, the objective function and inequality constraints do not need to be convex, and equality constraints do not need to be affine. A Hestenes–Powell augmented L...

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Bibliographic Details
Published inNeural networks Vol. 181; p. 106791
Main Authors Guan, Huimin, Liu, Yang, Kou, Kit Ian, Gui, Weihua
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.01.2025
Subjects
Algorithms
Computer Simulation
Distributed optimization
Hestenes–Powell augmented Lagrangian function
Humans
Models, Neurological
Neural Networks, Computer
Neurodynamic optimization
Neurodynamic systems
Nonconvex optimization
Hestenes–Powell augmented Lagrangian function
Distributed optimization
Nonconvex optimization
Neurodynamic systems
Neurodynamic optimization
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Summary:This paper develops a neurodynamic model for distributed nonconvex-constrained optimization. In the distributed constrained optimization model, the objective function and inequality constraints do not need to be convex, and equality constraints do not need to be affine. A Hestenes–Powell augmented Lagrangian function for handling the nonconvexity is established, and a neurodynamic system is developed based on this. It is proved that it is stable at a local optimal solution of the optimization model. Two illustrative examples are provided to evaluate the enhanced stability and optimality of the developed neurodynamic systems.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2024.106791