Treffer: Optimization algorithm based on densification and dynamic canonical descent
Title:
Optimization algorithm based on densification and dynamic canonical descent
Authors:
Source:
The international conference on computational methods in sciences and engineering 2004Journal of computational and applied mathematics. 191(2):269-279
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 8 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Programmation mathématique numérique, Numerical methods in mathematical programming, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, Algorithme compétitif, Competitive algorithms, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse numérique, Numerical analysis, Análisis numérico, Contrôle optimal, Optimal control (mathematics), Control óptimo (matemáticas), Fonction coût, Cost function, Función coste, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Méthode gradient, Gradient method, Método gradiente, Méthode optimisation, Optimization method, Método optimización, Méthode stochastique, Stochastic method, Método estocástico, Programmation mathématique, Mathematical programming, Programación matemática, Recuit simulé, Simulated annealing, Recocido simulado, Courbe densification, Densification curve, Méthode dérivée libre, Derivative free method, Optimisation globale, Réduction variable, Variable reduction, 65K05, 90C56 Global optimization, Densification curves, Derivative-free methods, Optimal control
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Avionics and Control Laboratory, Department of Aerospace Sciences, University of Beira Interior, 6201-001 Covilhã, Portugal
ISSN:
0377-0427
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
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:
Mathematics
Operational research. Management
Operational research. Management
Accession Number:
edscal.17689322
Database:
PASCAL Archive
Weitere Informationen
Stochastic methods have gained some popularity in global optimization in that most of them do not assume the cost functions to be differentiable. They have capabilities to avoid being trapped by local optima, and may converge even faster than gradient-based optimization methods on some problems. The present paper proposes an optimization method, which reduces the search space by means of densification curves, coupled with the dynamic canonical descent algorithm. The performances of the new method are shown on several known problems classically used for testing optimization algorithms, and proved to outperform competitive algorithms such as simulated annealing and genetic algorithms.