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

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.

Keywords:

• quantum-behaved particle swarm optimization
• estimation of distribution algorithm
• hybrid probability distribution
• diversity control
• mutual information
• image registration

2000 AMS Subject Classifications :

• 68T20
• 68U10
• 68W20
• 90C90
• 94A08

Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (No. JUSRP21012).