A Novel Particle Swarm Optimization Algorithm for Global Optimization
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-21
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed.
In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered.
Meanwhile, to avoid premature, an abandoned mechanism is used.
Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration.
To verify the performance of our algorithm, standard test functions have been employed.
The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.
American Psychological Association (APA)
Chun-Feng, Wang& Liu, Kui. 2016. A Novel Particle Swarm Optimization Algorithm for Global Optimization. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099821
Modern Language Association (MLA)
Chun-Feng, Wang& Liu, Kui. A Novel Particle Swarm Optimization Algorithm for Global Optimization. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1099821
American Medical Association (AMA)
Chun-Feng, Wang& Liu, Kui. A Novel Particle Swarm Optimization Algorithm for Global Optimization. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1099821
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099821