A dual population based firefly algorithm and its application on wireless sensor network coverage optimisation
by Gan Yu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 13, No. 3, 2017

Abstract: Firefly algorithm (FA) has shown good performance on many engineering optimisation problems. Recent study has pointed out that FA suffers from slow convergence. To enhance the performance of FA, this paper presents a dual population based FA (called DPFA). In DPFA, the entire population consists of two sub-populations. A memetic FA (MFA) and the standard differential evolution are used to generate new solutions in different sub-populations. To verify the performance of DPFA, we test it on nine benchmark functions. Simulation results show that DPFA outperforms MFA and other improved FA algorithms. Finally, we use the proposed DPFA to solve wireless sensor network coverage optimisation problems. Results show that DPFA can also achieve promising solutions.

Online publication date: Mon, 11-Dec-2017

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 Wireless and Mobile Computing (IJWMC):
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