基于量子混沌粒子群优化算法的分数阶超混沌系统参数估计

doi:10.3969/j.issn.0372-2112.2018.02.011

电子学报 ›› 2018, Vol. 46 ›› Issue (2): 333-340.DOI: 10.3969/j.issn.0372-2112.2018.02.011

所属专题：粒子群优化算法

基于量子混沌粒子群优化算法的分数阶超混沌系统参数估计

闫涛^1,5, 刘凤娴^2,3, 陈斌⁴

1. 山西大学计算机与信息技术学院, 山西太原 030006;
2. 中国科学院广州地球化学研究所, 广东广州 510640;
3. 中国科学院大学, 北京 100049;
4. 中国科学院广州电子技术研究所, 广东广州 510070;
5. 山西大学大数据科学与产业研究院, 山西太原 030006

收稿日期:2016-10-27 修回日期:2017-04-29 出版日期:2018-02-25
- 作者简介:
- 闫涛,男,1987年生于山西定襄.山西大学计算机与信息技术学院讲师.研究方向为群体智能、机器视觉与图像处理.E-mail:hongyanyutian@126.com;刘凤娴,女,1987年生于山西广灵.中国科学院广州地球化学研究所博士研究生.研究方向为大气环境化学.
- 基金资助:
- 中国科学院西部之光基金 (No.2011180）

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

YAN Tao^1,5, LIU Feng-xian^2,3, CHEN Bin⁴

1. School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China;
2. Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. Guangzhou, Guangdong 510640, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China;
4. Guangzhou Institute of Electronic Technology, Chinese Academy of Sciences. Guangzhou, Guangdong 510070, China;
5. Institute of Big Data Science and Industry, Shanxi University, Taiyuan, Shanxi 030006, China

Received:2016-10-27 Revised:2017-04-29 Online:2018-02-25 Published:2018-02-25
- Supported by:
- West Light Foundation of The Chinese Academy of Sciences (No.2011180)

摘要/Abstract

摘要： 为了对分数阶超混沌系统中的未知参数进行准确估计，提出一种量子混沌粒子群优化算法（Quantum chaos particle swarm optimization，QCPSO）.该算法通过对量子粒子群优化算法（Quantum behaved particle swarm optimization，QPSO）的实现机理进行分析，并结合量子纠缠与混沌系统之间的相关性而实现.首先，将量子势阱中心视为混沌吸引子围绕的不动点，处于吸引子外部的粒子会逐渐聚集于吸引子之内，而处于吸引子内部的粒子会出现快速分离扩散的现象；然后，采用基于随机映射的粒子更新机制，充分保证混沌粒子的初值多样性；最后，提出了基于不动点中心的尺度自适应策略，解决了算法后期的搜索停滞问题.运用QCPSO算法对典型分数阶超混沌系统参数进行估计，结果表明，该算法在收敛速度与精度上优于改进的差分进化算法、自适应人工蜂群算法以及改进的量子粒子群优化算法.

关键词: 量子粒子群优化算法, 混沌映射, 混沌吸引子, 分数阶超混沌系统

Abstract: 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.

Key words: quantum behaved particle swarm optimization, chaotic maps, strange attractor, fractional order hyper chaotic system

中图分类号:

TP18

闫涛, 刘凤娴, 陈斌. 基于量子混沌粒子群优化算法的分数阶超混沌系统参数估计[J]. 电子学报, 2018, 46(2): 333-340.

YAN Tao, LIU Feng-xian, CHEN Bin . New Quantum Chaos Particle Swarm Optimization Algorithm for Estimating the Parameter of Fractional Order Hyper Chaotic System[J]. Acta Electronica Sinica, 2018, 46(2): 333-340.

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基于量子混沌粒子群优化算法的分数阶超混沌系统参数估计

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

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