A nature inspired hybrid optimisation algorithm for dynamic environment with real parameter encoding Online publication date: Sun, 23-Jul-2017
by Ashish Tripathi; Nitin Saxena; K.K. Mishra; A.K. Misra
International Journal of Bio-Inspired Computation (IJBIC), Vol. 10, No. 1, 2017
Abstract: In recent years, many nature inspired algorithms have been proposed which are widely applicable for different optimisation problems. Real-world optimisation problems have become more complex and dynamic in nature and a single optimisation algorithm is not good enough to solve such type of problems individually. Thus hybridisation of two or more algorithms may be a fruitful effort in handling the limitations of individual algorithm. In this paper a hybrid optimisation algorithm has been established which includes the features of environmental adaption method for dynamic (EAMD) environment and particle swarm optimisation (PSO). This algorithm is specially designed to optimise both unimodal and multimodal problems and the performance is checked over a group of 24 benchmark functions provided by black box optimisation benchmarking (BBOB-2013). The result shows the superiority of this hybrid algorithm over other well established state-of-the-art algorithms.
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 Bio-Inspired Computation (IJBIC):
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