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Using selection to improve quantum-behaved particle swarm optimisation

Using selection to improve quantum-behaved particle swarm optimisation

Long, Haixia, Sun, Jun, Wang, Xiaogen, Lai, C.-H. ORCID: 0000-0002-7558-6398 and Xu, Wenbo (2009) Using selection to improve quantum-behaved particle swarm optimisation. International Journal of Innovative Computing and Applications, 2 (2). pp. 100-114. ISSN 1751-648X (Print), 1751-6498 (Online) (doi:https://doi.org/10.1504/IJICA.2009.031780)

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Abstract

Quantum-behaved particle swarm optimisation (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. This paper describes two selection mechanisms into QPSO to improve the search ability of QPSO. One is the QPSO with tournament selection (QPSO-TS) and the other is the QPSO with roulette-wheel selection (QPSO-RS). While the centre of position distribution of each particle in QPSO is determined by global best position and personal best position, in the QPSO with selection operation, the global best position is substituted by a candidate solution through selection. The QPSO with selection operation also maintains the mean best position of the swarm as in the previous QPSO to make the swarm more efficient in global search. The experiment results on benchmark functions show that both QPSO-RS and QPSO-TS have better performance and stronger global search ability than QPSO and standard PSO.

Item Type: Article
Additional Information: [1] Published in: International Journal of Innovative Computing and Applications, 2009, Vol. 2, No. 2. Special Issue: on Swarm-Based Computing: Foundation and Application. Guest Editors: Professor Zhihua Cui and Professor Jianchao Zeng
Uncontrolled Keywords: quantum-behaved particle swarm optimisation, QPSO, tournament selection, roulette-wheel selection, global best position, QPSO-TS, QPSO-R, search ability
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Computer software
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Related URLs:
Last Modified: 14 Oct 2016 09:22
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/9093

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