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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 9482073, 9 pages
Research Article

A Novel Particle Swarm Optimization Algorithm for Global Optimization

1Department of Mathematics, Henan Normal University, Xinxiang 453007, China
2Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics and Information Sciences, Henan Normal University, Xinxiang 453007, China

Received 17 August 2015; Revised 1 December 2015; Accepted 8 December 2015

Academic Editor: Massimo Panella

Copyright © 2016 Chun-Feng Wang and Kui Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.