DocumentCode :
527499
Title :
Improved hybrid particle swarm optimization algorithm
Author :
Zhang, Xiaofei ; Gao, Mingzheng ; Xiaofei Zhang
Author_Institution :
Coll. of Metrol. Technol. & Eng., China Jiliang Univ., Hangzhou, China
Volume :
5
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
2642
Lastpage :
2646
Abstract :
Particle swarm optimization algorithm (PSO, in short) is a heuristic global optimization algorithm based on swarm intelligence. Each particle of the swarm represents one candidate solution of the optimization problem. PSO searches the optimal region of optimization space through the interaction of particles. In this article, the PSO which has slow convergence rate and is easily trapped in local optimum region is modified by changing the velocity updating formula of PSO, adding the disturbance term, adding crossover and mutation operator to the algorithm so that the performance of the hybrid PSO is significantly improved. Some experimental results indicate that the improved PSO algorithm is effective and has good capability on both global and local optimization problems.
Keywords :
heuristic programming; particle swarm optimisation; PSO; crossover operator; global optimization problems; heuristic global optimization algorithm; improved hybrid particle swarm optimization algorithm; local optimization problems; local optimum region; mutation operator; swarm intelligence; Artificial neural networks; Computers; Convergence; Heuristic algorithms; Optimization; Particle swarm optimization; crossover operator; improved PSO; mutation operator; optimization algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5583000
Filename :
5583000
Link To Document :
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