DocumentCode :
2955495
Title :
Adaptive parameter control for quantum-behaved particle swarm optimization on individual level
Author :
Sun, Jun ; Xu, Wenbo ; Feng, Bin
Author_Institution :
Sch. of Inf. Technol., Southern Yangtze Univ., Wuxi, China
Volume :
4
fYear :
2005
fDate :
10-12 Oct. 2005
Firstpage :
3049
Abstract :
Particle swarm optimization (PSO) is a population-based evolutionary search technique, which has comparable performance with genetic algorithm. The existing PSOs, however, are not global-convergence-guaranteed algorithms. In the previous work, we proposed quantum-behaved particle swarm optimization (QPSO) algorithm that outperforms traditional PSOs in search ability as well as having less parameter to control. This paper focuses on discussing two adaptive parameter control methods for QPSO. After the ideology of QPSO is formulated, the experiment results of stochastic simulation are given to show how to select the parameter value to guarantee the convergence of the particle in QPSO. Finally, two adaptive parameter control methods are presented and experiment results on benchmark functions testify their efficiency.
Keywords :
adaptive control; particle swarm optimisation; adaptive method; adaptive parameter control; evolutionary search; genetic algorithm; global convergence; particle swarm optimization; quantum behavior; Adaptive control; Convergence; Equations; Genetic algorithms; Genetic programming; Information technology; Optimization methods; Particle swarm optimization; Programmable control; Sun; Particle Swarm Optimization; adaptive method; convergence; parameter control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9298-1
Type :
conf
DOI :
10.1109/ICSMC.2005.1571614
Filename :
1571614
Link To Document :
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