A new modified differential evolution for global optimisation
by Xuemei You; Yinghong Ma; Zhiyuan Liu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 10, No. 1, 2016

Abstract: Differential evolution (DE) is a population-based random optimisation algorithm, which has been used to solve benchmark functions and real-world optimisation problems. The DE has three important operators: mutation, crossover, and selection. The mutation operator in the original DE can hardly balance the exploitation and exploration of the search. In this paper, we design a new mutation operator to improve the exploitation ability of DE. Experiments are carried out on 13 classical test functions. Simulation results show that the new mutation scheme can help DE to find better solutions than three other classical DE mutation strategies.

Online publication date: Tue, 08-Mar-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 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