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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 326431, 12 pages
Research Article

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization

1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
2School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou 511483, China
3Department of Electronic Engineering, City University of Hong Kong, Tat Chee Ave, Hong Kong

Received 12 December 2014; Accepted 15 April 2015

Academic Editor: Thomas DeMarse

Copyright © 2015 Zhen-Lun Yang et al. 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.


An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.