Contraction-Expansion Coefficient Learning in Quantum-Behaved Particle Swarm Optimization

Author

Tian, Na ; Lai, Choi-Hong ; Pericleous, Koulis ; Sun, Jun ; Xu, Wenbo

Author_Institution

Sch. of Comput. & Math. Sci., Univ. of Greenwich, London, UK

fYear

2011

fDate

14-17 Oct. 2011

Firstpage

303

Lastpage

308

Abstract

Quantum-behaved particle swarm optimization was proposed from the view of quantum world and based on the particle swarm optimization, which has been proved to outperform the traditional PSO. The Expansion-Contraction coefficient is the only parameter in QPSO, which has great influence on the global search ability and convergence of the particles. In this paper, two parameter control methods are proposed. Numerical experiments on the benchmark functions are presented.

Keywords

particle swarm optimisation; quantum computing; contraction-expansion coefficient learning; global search ability; parameter control methods; quantum behaved particle swarm optimization; quantum world; Annealing; Artificial neural networks; Benchmark testing; Convergence; Educational institutions; Particle swarm optimization; Vectors; Contraction-Expansion coefficient; Quantum-behaved Particle Swarm Optimization; annealing function; cosine function;

fLanguage

English

Publisher

ieee

Conference_Titel

Distributed Computing and Applications to Business, Engineering and Science (DCABES), 2011 Tenth International Symposium on

Conference_Location

Wuxi

Print_ISBN

978-1-4577-0327-0

Type

conf

DOI

10.1109/DCABES.2011.32

Filename

6118721