• The impact of energy function...

Result: The impact of energy function structure on solving generalized assignment problem using hopfield neural network

Title:
The impact of energy function structure on solving generalized assignment problem using hopfield neural network
Authors:
MONFARED, M. A. S, ETEMADI, M
Source:
Inclunding a Feature Cluster on Mathematical Finance and Risk ManagementEuropean journal of operational research. 168(2):645-654
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Flots dans les réseaux. Problèmes combinatoires, Flows in networks. Combinatorial problems, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Décomposition fonction, Function decomposition, Descomposición función, Faisabilité, Feasibility, Practicabilidad, Fonction généralisée, Generalized function, Función generalizada, Fonction pénalité, Penalty function, Función penalidad, Fonction structure, Structure function, Función estructura, Fonction énergie, Energy function, Función energía, Modèle Hopfield, Hopfield model, Modelo Hopfield, Méthode pénalité, Penalty method, Método penalidad, Optimisation combinatoire, Combinatorial optimization, Optimización combinatoria, Optimisation sans contrainte, Unconstrained optimization, Optimización sin restricción, Optimisation sous contrainte, Constrained optimization, Optimización con restricción, Problème combinatoire, Combinatorial problem, Problema combinatorio, Réseau neuronal Hopfield, Hopfield neural nets, Réseau neuronal, Neural network, Red neuronal, Problème affectation, Assignment problem, Problema asignación, Augmented Lagrangean, Neural networks, Penalty methods
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Industrial of Engineering, School of Engineering, The University of Alzahra, Vanak St, Tehran 1993891176, Iran, Islamic Republic of
Department of Mathematics, The University of Ahahra, Tehran, Iran, Islamic Republic of
ISSN:
0377-2217
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17203427
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.17203427
Database:
PASCAL Archive

Further Information

In the last 20. years, neural networks researchers have exploited different penalty based energy functions structures for solving combinatorial optimization problems (COPs) and have established solutions that are stable and convergent. These solutions, however, have in general suffered from lack of feasibility and integrality. On the other hand, operational researchers have exploited different methods for converting a constrained optimization problem into an unconstrained optimization problem. In this paper we have investigated these methods for solving generalized assignment problems (GAPs). Our results concretely establishes that the augmented Lagrangean method can produce superior results with respect to feasibility and integrality, which are currently the main concerns in solving neural based COPs.