DocumentCode
3380245
Title
A fine tuning hybrid particle swarm optimization algorithm
Author
Tang, Jun ; Zhao, Xiaojuan
Author_Institution
Dept. of Inf. Eng., Hunan Urban Constr. Coll., XiangTan, China
fYear
2009
fDate
13-14 Dec. 2009
Firstpage
296
Lastpage
299
Abstract
Particle swarm optimization (PSO) has shown its good performance in many optimization problems. This paper introduces a new approach called hybrid particle swarm optimization like algorithm (HPSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). The performance of all the three PSO algorithms is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms.
Keywords
extrapolation; mathematical operators; particle swarm optimisation; PSO algorithm; extrapolated particle swarm optimization; fine tuning operators; hybrid particle swarm optimization algorithm; Acceleration; Algorithm design and analysis; Biomedical engineering; Educational institutions; Equations; Genetic algorithms; Genetic mutations; Optimal control; Particle swarm optimization; Performance analysis; PSO; cross-over operator; mutation operators; particle swarm optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
BioMedical Information Engineering, 2009. FBIE 2009. International Conference on Future
Conference_Location
Sanya
Print_ISBN
978-1-4244-4690-2
Electronic_ISBN
978-1-4244-4692-6
Type
conf
DOI
10.1109/FBIE.2009.5405908
Filename
5405908
Link To Document