基于云模型的自适应量子粒子群算法

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摘要

利用云模型理论能兼顾随机性和模糊性的特质, 提出一种基于云模型的自适应量子粒子群优化算法。首先分析量子粒子群算法的控制机制, 在此基础上, 使用云算子实现对每个粒子的吸收扩张因子自适应控制, 达到在进化过程中对粒子飞行位置动态调整的目的, 使算法具有较快的收敛速度和较强的全局搜索能力。同时, 补充针对性的优化方案, 有效避免算法陷入局部最优。对典型测试函数的仿真对比实验表明, 该算法具有寻优能力强、搜索精度高、稳定度好等优点, 相比其它同类算法具有一定优势。

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	马颖
	田维坚
	樊养余

关键词 ：云模型, 量子粒子群算法, 量子计算, 函数优化

Abstract：

Utilizing the characteristic of cloud model principles which can make good balance between the randomness and the fuzziness, an adaptive quantum-behaved particle swarm optimization algorithm based on cloud model is proposed. Firstly, the control mechanism of quantum-behaved particle swarm optimization algorithm is analyzed. On this basis, the absorption-expansion factor of each particle is adaptively controlled by cloud operators to achieve the dynamic adjustment to the positions of particles in evolutionary process. Thus, the proposed algorithm obtains a higher convergence speed and a stronger global search ability. Programs are modified for the targeted optimization to make the proposed algorithm effectively avoid falling into local optimum. The results of simulation experiments with typical test functions show that the proposed algorithm has advantages in search ability, accuracy and stability, and it is more effective than other similar algorithms.

Key words： Cloud Model Quantum-Behaved Particle Swarm Algorithm Quantum Computing Function Optimization

收稿日期: 2012-11-19

ZTFLH:

TP301.6

基金资助:

西安工业大学校长科研基金项目(No.XAGDXJJ1042)资助

作者简介: 马颖(通讯作者), 男, 1979年生, 工程师, 博士研究生, 主要研究方向为量子信息、信号处理等.E-mail:innovator@163.com.田维坚, 男, 1957年生, 博士, 研究员, 主要研究方向为光电工程.樊养余, 男, 1960年生, 博士, 教授, 主要研究方向为模式识别、虚拟现实等。

引用本文:

马颖, 田维坚, 樊养余. 基于云模型的自适应量子粒子群算法[J]. 模式识别与人工智能, 2013, 26(8): 787-793. Ma-Ying, TIAN Wei-Jian, FAN Yang-Yu. Adaptive Quantum-Behaved Particle Swarm Optimization Algorithm Based on Cloud Model. , 2013, 26(8): 787-793.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/ 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2013/V26/I8/787

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