A novel constraint multi-objective artificial physics optimisation algorithm and its convergence
by Yan Wang, Jian-Chao Zeng, Zhi-Hua Cui, Xiao-Juan He
International Journal of Innovative Computing and Applications (IJICA), Vol. 3, No. 2, 2011

Abstract: This paper presents a constraint multi-objective artificial physics optimisation (CMOAPO) algorithm by introducing a novel optimisation paradigm called artificial physics optimisation (APO) into constraint multi-objective domain. Combining with characteristics of constraint multi-objective optimisation problems, a method of virtual force decreasing is incorporated into CMOAPO to decrease the probability of individuals moving from feasible region into infeasible region. Furthermore, the convergence of CMOAPO is analysed in terms of theory with related knowledge of probability. The performance of CMOAPO algorithm is tested using several benchmark functions. The results obtained show that the proposed approach is effective.

Online publication date: Sat, 21-Mar-2015

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