Skip navigation

Contraction-expansion coefficient learning in quantum-behaved particle swarm optimization

Contraction-expansion coefficient learning in quantum-behaved particle swarm optimization

Tian, Na, Lai, Choi-Hong, Pericleous, Koulis, Sun, Jun and Xu, Wenbo (2011) Contraction-expansion coefficient learning in quantum-behaved particle swarm optimization. In: Proceedings: 2011 Tenth International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2011). Conference Publishing Services, Los Alamitos, CA, USA, pp. 303-308. ISBN 9780769544151 (doi:10.1109/DCABES.2011.32)

Full text not available from this repository.

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.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings: 2011 Tenth International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2011)
Additional Information: [1] This paper was first presented at the 2011 Tenth International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2011), held from 14-17 October 2011 in Wuxi, Jiangsu, China.
Uncontrolled Keywords: quantum-behaved particle swarm optimization, contraction-expansion coefficient, cosine function, annealing function
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/7599

Actions (login required)

View Item View Item