Control scaling factor of cuckoo search algorithm using learning automata
by Yaohua Lin; Lijin Wang; Yiwen Zhong; Cuiping Zhang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 7, No. 5, 2016

Abstract: In this study, we seek an optimal scaling factor of cuckoo search algorithm by using learning automata. In the presented method, the same learning automaton is built for each individual, and a set of actions of each learning automaton are set to several constant scaling factors. Moreover, the linear reward-penalty learning algorithm is used in learning automaton to select the optimal scaling factor of each individual. Extensive experiments on 20 benchmark functions demonstrate better effectiveness and efficiency of controlling scaling factor of cuckoo search by using learning automata.

Online publication date: Tue, 01-Nov-2016

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 Computing Science and Mathematics (IJCSM):
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