Accelerated grey wolf optimiser for continuous optimisation problems Online publication date: Mon, 06-Apr-2020
by Shubham Gupta; Kusum Deep; Seyedali Mirjalili
International Journal of Swarm Intelligence (IJSI), Vol. 5, No. 1, 2020
Abstract: Grey wolf optimiser (GWO) is a relatively simple and efficient nature-inspired optimisation algorithm which has shown its competitive performance compared to other population-based meta-heuristics. This algorithm drives the solutions towards some of the best solutions obtained so far using a unique mathematical model, which is inspired from leadership behaviour of grey wolves in nature. To combat the issue of premature convergence and local optima stagnation, an enhanced version of GWO is proposed in this paper. The proposed algorithm is named accelerated grey wolf optimiser (A-GWO). In A-GWO, novel modified search equations are developed that enhances the exploratory behaviour of wolves at later generations, and the exploitation of search space is also improved in the whole search process. To validate the performance of the proposed algorithm, set of 23 well-known classical benchmark problems are used. The results and comparison through various metrics show the reliability and efficiency of the A-GWO.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Swarm Intelligence (IJSI):
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 subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook