Particle swarm optimisation algorithm with forgetting character
by Dai-lin Yuan, Qiu Chen
International Journal of Bio-Inspired Computation (IJBIC), Vol. 2, No. 1, 2010

Abstract: In order to improve the performance of particle swarm optimisation algorithm in the complicated function optimisation, a new improved measure was advanced. The new algorithm only memorised the individual information of finite steps in the iterations and utilised the average information of swarm. Due to the individuals forgetting the former best positions, the forgetting character was hold. The ability of exploration was improved because of using forgetting character and average information of swarm. The simulations of complicated function optimisation show that the new algorithm can find the global best solution more easily than the common particle swarm optimisation algorithm.

Online publication date: Thu, 03-Dec-2009

The full text of this article 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 Bio-Inspired Computation (IJBIC):
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