Particle swarm optimisation with differential mutation Online publication date: Wed, 06-Mar-2013
by Tapas Si; Nanda Dulal Jana
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 11, No. 3/4, 2012
Abstract: Particle swarm optimisation (PSO) is population-based optimisation algorithm having stochastic in nature. PSO has quick convergence speed but often gets stuck into local optima due to lacks of diversity. In this work, first mutation operator adopted from Differential Evolution (DE) algorithm is applied in PSO with decreasing inertia weight (PSO-DMLB). In second method, DE mutation is applied in another PSO variant, namely Comprehensive Learning PSO (CLPSO). The second method is termed as CLPSO-DMLB. Local best position of each particle is muted by a predefined mutation probability with the scaled difference of two randomly selected particle's local best position to increase the diversity in the population to achieve better quality of solutions. The proposed methods are applied on well-known benchmark unconstrained functions and obtained results are compared to show the effectiveness of the proposed methods.
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 Intelligent Systems Technologies and Applications (IJISTA):
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