k-Means clustering-based evolutionary algorithm for solving optimisation problems Online publication date: Thu, 11-Nov-2021
by Tribhuvan Singh; Krishn Kumar Mishra; Ranvijay
International Journal of Forensic Engineering (IJFE), Vol. 5, No. 2, 2021
Abstract: Environmental adaptation method (EAM) is a newly developed optimisation algorithm for complex problems. Although EAM and its variants converge very fast in lower-dimensional problems, the performance of these algorithms falls drastically in higher-dimensional problems. This paper introduces a novel approach to improve the performance of the algorithm in higher-dimensional problems. In order to explore the whole search space, the problem search space is divided into multiple mutually exclusive clusters, and then parallel exploitation and exploration are achieved that produces better results. The solutions of independent clusters try to adopt a more suitable structure using the direction received from the local/global best and local/global worst solutions. The performance of the suggested algorithm is compared with other existing algorithms using the benchmark function of the COmparing Continuous Optimisers (COCO) framework. The experimental results have demonstrated that the proposed algorithm performs well in many ways.
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 Forensic Engineering (IJFE):
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