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计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

双心扰动量子粒子群优化算法研究

王安龙,何建华,陈 松,刘怀远   

  1. (西北工业大学电子信息学院,西安 710000)
  • 收稿日期:2013-04-17 出版日期:2014-07-15 发布日期:2014-07-14
  • 作者简介:王安龙(1988-),男,硕士研究生,主研方向:智能计算,任务规划;何建华,副教授;陈 松、刘怀远,硕士研究生。

Research on QPSO Algorithm of Double Core Disturbance

WANG An-long, HE Jian-hua, CHEN Song, LIU Huai-yuan   

  1. (School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710000, China)
  • Received:2013-04-17 Online:2014-07-15 Published:2014-07-14

摘要: 针对量子粒子群优化算法早熟收敛的问题,提出一种双心扰动的变异机制。对粒子的势能中心和粒子群的重心进行自适应柯西变异,发挥两者在进化后期的协同引导能力,以提高进化后期粒子群对新空间的开拓能力。对4个典型测试函数进行仿真实验,结果表明,对于单峰函数优化,双心扰动变异机制的优化效果比只采用势能中心、重心和全局最好位置变异的优化效果提高36.42%以上;对于多峰函数优化,其优化效果提高32.84%以上。

关键词: 量子粒子群优化算法, 势能中心, 全局最好位置, 柯西变异, 函数优化

Abstract: Aiming at the problem of the premature convergence of Quantum Particle Swarm Optimization(QPSO) algorithm. This paper introduces a double core disturbance mutation mechanism. It uses adaptive Cauchy mutation to mutate the potential energy of the particle center and the center of gravity of the particle swarm and make full use of the guiding ability of the two centers in the late part of evolution. It adopts four typical functions to conduct simulation experiment, results show that double core disturbances mutation mechanism optimization is better than the strategy of only potential energy center, the center of gravity or global optimal mutation at least 36.42%, and the optimization results improve at least 32.84% for multimodal function optimization.

Key words: Quantum Particle Swarm Optimization(QPSO) algorithm, potential energy center, global best position, Cauchy mutation, unction optimization

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