计算机工程 ›› 2010, Vol. 36 ›› Issue (13): 175-177.doi: 10.3969/j.issn.1000-3428.2010.13.062

• 人工智能及识别技术 • 上一篇    下一篇

一种改进变尺度混沌优化的模糊量子遗传算法

滕 皓1,2,曹爱增1,杨炳儒2   

  1. (1. 济南大学信息科学与工程学院,济南 250022;2. 北京科技大学信息工程学院,北京 100083)
  • 出版日期:2010-07-05 发布日期:2010-07-05
  • 作者简介:滕 皓(1970-),男,副教授、博士研究生,主研方向:遗传算法,数据挖掘;曹爱增,副教授、硕士;杨炳儒,教授、博士生导师
  • 基金项目:
    国家自然科学基金资助项目(60675030)

Ameliorated Mutative Scale Chaos Optimization Fuzzy Quantum Genetic Algorithm

TENG Hao1,2, CAO Ai-zeng1, YANG Bing-ru2   

  1. (1. School of Information Science and Engineering, University of Jinan, Jinan 250022; 2. School of Information Engineering, University of Science and Technology Beijing, Beijing 100083)
  • Online:2010-07-05 Published:2010-07-05

摘要: 针对量子遗传算法存在的易陷入局部极小等问题,提出一种模糊量子遗传算法。该算法采用一种变尺度混沌优化方法,只需设 2个循环,内循环进行混沌搜索,外循环负责缩小区间,通过改进它的收敛策略,可以避免混沌优化在区间内的盲目重复搜索。利用改进的变尺度混沌优化方法,对量子遗传操作产生的种群进行混沌搜索寻优,同时模糊控制更新,加快种群的进化。仿真结果表明,该方法的寻优效果优于量子遗传算法及遗传算法。

关键词: 量子遗传算法, 混沌优化, 收敛策略, 变尺度, 模糊控制

Abstract: Aiming at the problem of Quantum Genetic Algorithm(QGA) exists easily getting into local minimum. this paper presents a fuzzy QGA. It adopts a mutative scale chaos optimization method. The method has nested iterations, the inner is used for chaos search and the outer is used to reduce the range. The blind repeated search of chaos optimization in search space can be avoided through ameliorating its constringency strategy. Chaotic search for the optimization using this chaos optimization method is implemented towards the population produced by the quantum genetic algorithm, and fuzzy control is updated the population in order to speed the evolution of the population. Simulation results show that this algorithm is more effective than QGA and genetic algorithm.

Key words: Quantum Genetic Algorithm(QGA), chaos optimization, constringency strategy, mutative scale, fuzzy control

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