Parameters identification of chaotic systems by quantum-behaved particle swarm optimization

Abstract

This paper applies a novel evolutionary optimization algorithm named quantum-behaved particle swarm optimization (QPSO) to estimate the parameters of chaotic systems, which can be formulated as a multimodal numerical optimization problem with high dimension from the viewpoint of optimization. Moreover, in order to improve the performance of QPSO, an adaptive mechanism is introduced for the parameter beta of QPSO. Finally, numerical simulations are provided to show the effectiveness and efficiency of the modified QPSO method.

Keywords:

• quantum-behaved particle swarm optimization
• optimization
• parameter identification
• chaotic system

2000 AMS Subject Classifications :

• 65K10
• 68W20
• 68T20
• 93B30

This article is referred to by:

Comment on ‘Parameters identification of chaotic systems by quantum-behaved particle swarm optimization’ [Int. J. Comput. Math. 86(12) (2009), pp. 2225–2235]