• Particle swarm optimization: H...

Result: Particle swarm optimization: Hybridization perspectives and experimental illustrations

Title:
Particle swarm optimization: Hybridization perspectives and experimental illustrations
Authors:
THANGARAJ, Radha, PANT, Millie, ABRAHAM, Ajith, BOUVRY, Pascal
Source:
Applied mathematics and computation. 217(12):5208-5226
Publisher Information:
Amsterdam: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 90 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, 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, Equations algébriques et transcendantes non linéaires, Nonlinear algebraic and transcendental equations, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse numérique, Numerical analysis, Análisis numérico, Calcul variationnel, Variational calculus, Cálculo de variaciones, Classification, Clasificación, Equation algébrique, Algebraic equation, Ecuación algebraica, Equation non linéaire, Non linear equation, Ecuación no lineal, Equation transcendante, Transcendental equation, Ecuación trascendente, Etude méthode, Method study, Estudio método, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Méthode optimisation, Optimization method, Método optimización, Programmation mathématique, Mathematical programming, Programación matemática, Résolution problème, Problem solving, Resolución problema, Solution globale, Global solution, Solución global, 49XX, 65H20, 65K10, 65Kxx, Algorithme QR, OR algorithm, Differential evolution, Genetic algorithms, Hybridization, Particle swarm optimization
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Faculty of Science, Technology and Communications, University of Luxembourg, Luxembourg
Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee 247667, India
Machine Intelligent Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, United States
ISSN:
0096-3003
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=23885032
Rights:
Copyright 2015 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.23885032
Database:
PASCAL Archive

Further Information

Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods, In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or more) techniques in a judicious manner such that the resulting algorithm contains the positive features of both (or all) the algorithms. Depending on the algorithm/s used we made three classifications as (i) Hybridization of PSO and genetic algorithms (ii) Hybridization of PSO with differential evolution and (iii) Hybridization of PSO with other techniques. Where, other techniques include various local and global search methods. Besides giving the review we also show a comparison of three hybrid PSO algorithms; hybrid differential evolution particle swarm optimization (DE-PSO), adaptive mutation particle swarm optimization (AMPSO) and hybrid genetic algorithm particle swarm optimization (GA-PSO) on a test suite of nine conventional benchmark problems.