Barebones particle swarm for multi-objective optimisation problems
by Yong Zhang, Dun-wei Gong, Ya-nan Jiang
International Journal of Innovative Computing and Applications (IJICA), Vol. 2, No. 2, 2009

Abstract: Control parameters, inertia weight and acceleration coefficients influence strongly performance of multi-objective particle swarm optimisation (MOPSO) algorithms. To eliminate the need for tuning of these parameters for different optimisation problems, this paper presents an almost parameter-free MOPSO algorithm, in which the concept of barebones particle swarm is incorporated into MOPSO. A special mutation operator that enriches the exploratory capabilities of our algorithm is also introduced. The proposed algorithm is validated using several benchmark test problems and four standard metrics. Results indicate that the proposed algorithm is highly competitive, and that can be considered a viable alternative to solving multi-objective optimisation problems.

Online publication date: Wed, 24-Feb-2010

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 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