Result: MCPSO : A multi-swarm cooperative particle swarm optimizer

Title:
MCPSO : A multi-swarm cooperative particle swarm optimizer
Authors:
BEN NIU, YUNLONG ZHU, XIAOXIAN HE, WU, Henry
Source:
Special issue on intelligent computing theory and methodologyApplied mathematics and computation. 185(2):1050-1062
Publisher Information:
New York, NY: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 25 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, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Algorithme, Algorithm, Algoritmo, Analyse numérique, Numerical analysis, Análisis numérico, Benchmark, Benchmarks, Connaissance, Knowledge, Conocimiento, Diversité, Diversity, Diversidad, Ecosystème, Ecosystem, Ecosistema, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Méthode optimisation, Optimization method, Método optimización, Norme, Standards, Norma, Optimisation, Optimization, Optimización, Particule, Particle, Partícula, Performance algorithme, Algorithm performance, Resultado algoritmo, Population, Población, Programmation mathématique, Mathematical programming, Programación matemática, Simulation statistique, Statistical simulation, Simulación estadística, 49XX, 65Kxx, MCPSO, Master-slave model, Particle swarm optimization: Multi-swarm
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office II. Nanta Street 114#, Donling District. Shenyang 110016, China
Graduate School of the Chinese Academy of Sciences, Beijing 100049, China
Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool L69 3GJ, United Kingdom
ISSN:
0096-3003
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18637811
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
Notes:
Mathematics
Accession Number:
edscal.18637811
Database:
PASCAL Archive

Further Information

This paper presents a new optimization algorithm - MCPSO, multi-swarm cooperative particle swarm optimizer. inspired by the phenomenon of symbiosis in natural ecosystems. MCPSO is based on a master-slave model, in which a population consists of one master swarm and several slave swarms. The slave swarms execute a single PSO or its variants independently to maintain the diversity of particles, while the master swarm evolves based on its own knowledge and also the knowledge of the slave swarms. According to the co-evolutionary relationship between master swarm and slave swarms, two versions of MCPSO are proposed, namely the competitive version of MCPSO (COM-MCPSO) and the collaborative version of MCPSO (COL-MCPSO), where the master swarm enhances its particles based on an antagonistic scenario or a synergistic scenario, respectively. In the simulation studies, several benchmark functions are performed, and the performances of the proposed algorithms are compared with the standard PSO (SPSO) and its variants to demonstrate the superiority of MCPSO.