The full text of this article

 

Using selection to improve quantum-behaved particle swarm optimisation
by Haixia Long, Jun Sun, Xiaogen Wang, C-H. Lai, Wenbo Xu
International Journal of Innovative Computing and Applications (IJICA), Vol. 2, No. 2, 2009

 

Abstract: Quantum-behaved particle swarm optimisation (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. This paper describes two selection mechanisms into QPSO to improve the search ability of QPSO. One is the QPSO with tournament selection (QPSO-TS) and the other is the QPSO with roulette-wheel selection (QPSO-RS). While the centre of position distribution of each particle in QPSO is determined by global best position and personal best position, in the QPSO with selection operation, the global best position is substituted by a candidate solution through selection. The QPSO with selection operation also maintains the mean best position of the swarm as in the previous QPSO to make the swarm more efficient in global search. The experiment results on benchmark functions show that both QPSO-RS and QPSO-TS have better performance and stronger global search ability than QPSO and standard PSO.

Online publication date: Wed, 24-Feb-2010

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Innovative Computing and Applications (IJICA):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com