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An improved quantum-behaved particle swarm optimization and its application to medical image registration

An improved quantum-behaved particle swarm optimization and its application to medical image registration

Zhou, Di, Sun, Jun, Lai, Choi-Hong, Xu, Wenbo and Lee, Xiaoguang (2011) An improved quantum-behaved particle swarm optimization and its application to medical image registration. International Journal of Computer Mathematics, 88 (6). pp. 1208-1223. ISSN 0020-7160 (Print), 1029-0265 (Online)

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Abstract

This paper investigates the quantum-behaved particle swarm optimization (QPSO) algorithm from the perspective of estimation of distribution algorithm (EDA) which reveals the reason of QPSO's superiority. A revised QPSO (RQPSO) technique with a novel iterative equation is also proposed. The modified technique is deduced from the distribution function of the sum of two random variables with exponential and normal distribution, respectively. We present a diversity-controlled RQPSO (DRQPSO) algorithm, which helps prevent the evolutionary algorithms’ tendency to be easily trapped into local optima as a result of rapid decline in diversity. Both the RQPSO and DRQPSO are tested on three benchmark functions, as well as in medical image registration for performance comparison with the particle swarm optimization and QPSO.

Item Type: Article
Uncontrolled Keywords: quantum-behaved particle swarm optimization, estimation of distribution algorithm, hybrid probability distribution, diversity control, mutual information, image registration
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Related URLs:
Last Modified: 14 Oct 2016 09:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/7539

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