A comparison of different transfer functions for binary version of grey wolf optimiser Online publication date: Thu, 11-Jan-2018
by Shuqin Wang; Gang Hua; Guosheng Hao; Chunli Xie
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 13, No. 4, 2017
Abstract: Grey Wolf Optimiser (GWO) is the recently proposed meta-heuristic algorithm inspired by grey wolves. The original version of GWO has been proposed to solve problems in continuous search spaces. However, there are many optimisation problems in discrete binary search spaces such as feature selection and dimensionality reduction. We applied eight transfer functions in our BGWO and compared their performance. The test functions whose minimum points are gotten at 0 are selected as bench functions but also those functions their minimum value are gotten at 1.
Online publication date: Thu, 11-Jan-2018
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 Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and 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 firstname.lastname@example.org