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Comput Intell Neurosci. 2015;2015:326431. doi: 10.1155/2015/326431. Epub 2015 May 10.

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

Author information

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

Abstract

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.

PMID:
26064085
PMCID:
PMC4442022
DOI:
10.1155/2015/326431
[Indexed for MEDLINE]
Free PMC Article

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