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
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;
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
DOI :
10.1109/FBIE.2009.5405908