Fractional Lévy flight bat algorithm for global optimisation Online publication date: Tue, 07-Apr-2020
by Redouane Boudjemaa; Diego Oliva; Fatima Ouaar
International Journal of Bio-Inspired Computation (IJBIC), Vol. 15, No. 2, 2020
Abstract: A well-known metaheuristic is the bat algorithm (BA), which consists of an iterative learning process inspired by bats echolocation behaviour in searching for prays. Basically, the BA uses a predefined number of bats that collectively move on the search space to find the global optimum. This article proposes the fractional Lévy flight bat algorithm (FLFBA), which is an improved version of the classical BA. In the FLFBA the velocity is updated through fractional calculus and a local search procedure that uses a random walk based on Lévy distribution. Such modifications enhance the ability of the algorithm to escape from local optimal values. The FLFBA has been tested using several well-known benchmark functions and its convergence is also compared with other evolutionary algorithms from the state-of-the-art. The results indicate that the FLFBA provided in several cases better performance in comparison to the selected evolutionary 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