Adaptive penalty-based neurodynamic approach for nonsmooth interval-valued optimization problem.
Neural Netw
; 176: 106337, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-38688071
ABSTRACT
The complex and diverse practical background drives this paper to explore a new neurodynamic approach (NA) to solve nonsmooth interval-valued optimization problems (IVOPs) constrained by interval partial order and more general sets. On the one hand, to deal with the uncertainty of interval-valued information, the LU-optimality condition of IVOPs is established through a deterministic form. On the other hand, according to the penalty method and adaptive controller, the interval partial order constraint and set constraint are punished by one adaptive parameter, which is a key enabler for the feasibility of states while having a lower solution space dimension and avoiding estimating exact penalty parameters. Through nonsmooth analysis and Lyapunov theory, the proposed adaptive penalty-based neurodynamic approach (APNA) is proven to converge to an LU-solution of the considered IVOPs. Finally, the feasibility of the proposed APNA is illustrated by numerical simulations and an investment decision-making problem.
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MEDLINE
Assunto principal:
Algoritmos
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article