Treffer: Parameter selection and adaptation in Unified Particle Swarm Optimization
Title:
Parameter selection and adaptation in Unified Particle Swarm Optimization
Authors:
Source:
Proceedings of the International Conference on Computational Methods in Sciences and Engineering 2004Mathematical and computer modelling. 46(1-2):198-213
Publisher Information:
Oxford: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 26 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 mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, 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, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Algorithme, Algorithm, Algoritmo, Analyse assistée, Computer aided analysis, Análisis asistido, Analyse numérique, Numerical analysis, Análisis numérico, Calcul scientifique, Scientific computation, Computación científica, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Modèle mathématique, Mathematical model, Modelo matemático, Méthode optimisation, Optimization method, Método optimización, Méthode particulaire, Particle method, Método partícula, Méthode stochastique, Stochastic method, Método estocástico, Performance algorithme, Algorithm performance, Resultado algoritmo, Programmation mathématique, Mathematical programming, Programación matemática, 49XX, 65C35, 65Kxx, Parameter adaptation, Parameter selection, Particle Swarm Optimization, Unified Particle Swarm Optimization
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras, 26110 Patras, Greece
University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, 26110 Patras, Greece
University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, 26110 Patras, Greece
ISSN:
0895-7177
Rights:
Copyright 2007 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
Accession Number:
edscal.18794179
Database:
PASCAL Archive
Weitere Informationen
The performance of the recently proposed Unified Particle Swarm Optimization method is investigated under different schemes for the determination and adaptation of the unification factor, which is the main parameter of the method, controlling its exploration and exploitation properties. Widely used benchmark problems are employed and numerous experiments are conducted along with statistical tests to yield useful conclusions regarding the effect of the parameter on the algorithm's performance as well as the most efficient adaptation schemes.