DocumentCode :
3510336
Title :
Convergence Analysis of a Dynamic Discrete PSO Algorithm
Author :
Luo Guilan ; Zhao Hai ; Song Chunhe
Author_Institution :
Province Key Lab. of Embedded Technol., Northeastern Univ., Shenyang
fYear :
2008
fDate :
1-3 Nov. 2008
Firstpage :
89
Lastpage :
92
Abstract :
The particle swarm optimization (PSO) algorithm has exhibited good performance on continuous optimization problems in static environment. However, there are lots of real-world optimization problems that are dynamic and discrete, which is a new research field of PSO. So a dynamic discrete PSO (DDPSO) algorithm is proposed in this paper. In this algorithm, we design a new strategy of environmental monitoring and response. When environment is changed, it can be apperceived by the change of fitness and position of particles and be responded by environment sensitivity and environmental change gene in time. Finally, to analyze the convergence of DDPSO based on the solving of zero state response in discrete-time systems, we get its convergence condition and motion track of particles. As a result, we find that DDPSO has good convergence and diversity of swarm owing to environmental change gene which has randomicity and variability.
Keywords :
convergence; discrete time systems; particle swarm optimisation; convergence analysis; discrete-time system; dynamic discrete PSO algorithm; environment sensitivity; environmental change; environmental monitoring; environmental response; particle swarm optimization; zero state response; Algorithm design and analysis; Convergence; Heuristic algorithms; Intelligent networks; Intelligent systems; Monitoring; Motion analysis; Particle swarm optimization; Particle tracking; Performance analysis; Convergence; Discrete-Dynamic Environment; Particle Swarm Optimization; Swarm Intelligence;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Networks and Intelligent Systems, 2008. ICINIS '08. First International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3391-9
Electronic_ISBN :
978-0-7695-3391-9
Type :
conf
DOI :
10.1109/ICINIS.2008.100
Filename :
4683175
Link To Document :
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