New Quantum Chaos Particle Swarm Optimization Algorithm for Estimating the Parameter of Fractional Order Hyper Chaotic System

被引：0
|
作者：
Yan T. [1 ,5 ]
Liu F.-X. [2 ,3 ]
Chen B. [4 ]
机构：
[1] School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi
[2] Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, Guangdong
[3] University of Chinese Academy of Sciences, Beijing
[4] Guangzhou Institute of Electronic Technology, Chinese Academy of Sciences, Guangzhou, 510070, Guangdong
[5] Institute of Big Data Science and Industry, Shanxi University, Taiyuan, 030006, Shanxi
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2018年 / 46卷 / 02期
关键词：
Chaotic maps; Fractional order hyper chaotic system; Quantum behaved particle swarm optimization; Strange attractor;
D O I：
10.3969/j.issn.0372-2112.2018.02.011
中图分类号：
学科分类号：
摘要：
A new quantum chaos particle swarm optimization (QCPSO) was proposed to accurately estimate the uncertain parameters of the fractional order hyper chaotic system.The QCPSO algorithm was realized by analyzing the mechanism of quantum behaved particle swarm optimization (QPSO) and combining the correlation between quantum entanglement and chaotic system.Firstly, the center of potential well was replaced by a fixed point of chaotic attractor.The particles which outside the attractor were gradually converged to the attractor, and the particles which inside the attractor were quickly diffused.Secondly, in order to guarantee the diversity of the initial value of the chaotic particles, the particle update mechanism based on random mapping was proposed.Finally, a scale adaptive strategy was proposed to solve the problem of search stagnation of the algorithm.The parameters of fractional order hyper chaotic system were estimated by the QCPSO algorithm, and the results showed that the QCPSO algorithm has faster convergence speed and higher accuracy than improved differential evolution algorithm, adaptive artificial bee colony algorithm and improved QPSO algorithm. © 2018, Chinese Institute of Electronics. All right reserved.
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页码：333 / 340
页数：7
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