Title :
A Hybrid Variants Particle Swarm Optimization Algorithm
Author_Institution :
Dept. of Inf. Eng., Hunan Urban Constr. Coll., Xiangtan, China
Abstract :
A hybrid variants particle swarm optimization (HVPSO) algorithm is proposed in this paper. HVPSO is a stand particle swarm optimization algorithm including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants to make the convergence faster. Two different HVPSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate that the GLbest-PSO provides better results for all the cross-over variants than that of the cPSO.
Keywords :
Acceleration; Birds; Educational institutions; Genetic algorithms; Machine vision; Man machine systems; Marine animals; Optimal control; Particle swarm optimization; Root mean square; global-local best; hybrid variants; particle swarm optimization; root mean square;
Conference_Titel :
Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on
Conference_Location :
Kaifeng, China
Print_ISBN :
978-1-4244-6595-8
Electronic_ISBN :
978-1-4244-6596-5
DOI :
10.1109/MVHI.2010.80
