Enhanced social emotional optimisation algorithm with generalised opposition-based learning Online publication date: Thu, 19-Feb-2015
by Zhaolu Guo; Xuezhi Yue; Kejun Zhang; Changshou Deng; Songhua Liu
International Journal of Computing Science and Mathematics (IJCSM), Vol. 6, No. 1, 2015
Abstract: Social emotional optimisation algorithm (SEOA) is a newly developed evolutionary algorithm, which exhibits excellent performance for various engineering problems in real-world applications. However, SEOA may easily trap into local optima when solving complex multimodal function optimisation problems. This paper proposes a novel social emotional optimisation algorithm, called GOSEOA, which performs the generalised opposition-based learning (GOBL) strategy with a certain probability during the evolution process. The proposed algorithm uses the generalised opposition-based learning strategy to transform the current population to a generalised opposition-based population. Accordingly, the current population and the generalised opposition-based population are simultaneously considered to increase the probability for finding the global optimum. Experiments conducted on a comprehensive set of benchmark functions indicate that GOSEOA can obtain promising performance on the majority of the test functions.
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 Computing Science and Mathematics (IJCSM):
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