A novel particle swarm optimiser with semi-active congregation and a nature-inspired supervising algorithm Online publication date: Mon, 22-Sep-2014
by Mohammad Kazem Akhlaghi; Alireza Fathi; Hamidreza Mohamadi Danyali; Mohamad Ali Khesali
International Journal of Computational Science and Engineering (IJCSE), Vol. 9, No. 5/6, 2014
Abstract: Particle swarm optimiser (PSO) is one of the most accepted evolutionary computation algorithms that have attracted incremental interest from scientists and engineers. The paper presents a new particle swarm optimisation technique combined with a supervising algorithm for controlling the particle velocity. The proposed variant, which is called PSO with semi-active congregation (PSOSAC), is a mathematical model of animals' congregation behaviour in nature. Observing the response of a school of fish in facing an attack inspired the authors to develop a supervising algorithm for adjusting the particles' velocity. Also the proposed algorithm allows narrowing the search space. The authors evaluate the proposed algorithm with ten different benchmarks, and compare it to other well-known PSO variants. The results are very promising, demonstrating superior results when compared to other techniques, and a good convergence rate.
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 Computational Science and Engineering (IJCSE):
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