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J4 ›› 2011, Vol. 46 ›› Issue (5): 97-102.

• SEWM 2011 会议 • 上一篇    下一篇

一种求解属性约简优化的协同粒子群算法

丁卫平1,2,3,王建东2,段卫华2,施佺1   

  1. 1.南通大学杏林学院, 江苏 南通 226019;
    2.南京航空航天大学计算机科学与技术学院, 江苏 南京 210016;
    3.苏州大学江苏省计算机信息处理技术重点实验室, 江苏 苏州 215006
  • 收稿日期:2010-12-06 发布日期:2011-05-25
  • 作者简介:丁卫平(1979- ),男,讲师,博士研究生,主要研究方向为机器学习、进化计算和数据挖掘等.Email:ding-wp@nuaa.edu.cn
  • 基金资助:

    国家高技术研究发展计划(863计划)重点资助项目(2006AA12A106);南通大学杏林学院自然科学科研项目(2010K123);苏州大学江苏省计算机信息处理技术重点实验室开放课题项目(KJS1023);江苏省自然科学基金研究项目(BK2010280)

Research of cooperative PSO for attribute reduction optimization

DING Wei-ping1,2,3, WANG Jian-dong2, DUAN Wei-hua2, SHI Quan1   

  1. 1. Xinlin College, Nantong University, Nantong 226019, Jiangsu, China;
    2.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 
    Nanjing 210016, Jiangsu, China; 3. Provincial Key Laboratory for Computer Information Processing Technology,
    Soochow University, Suzhou 215006, Jiangsu, China
  • Received:2010-12-06 Published:2011-05-25

摘要:

针对粗糙属性约简优化问题,利用粒子群寻求最优解的优势,提出一种改进的粗糙集属性约简优化的协同粒子群算法(AR-CPSO)。在最优属性寻求过程中,该算法使粒子群在属性空间通过约简集向量的分解和邻域簇的协同学习提高其寻优能力,并利用自适应约束强化罚函数较好地收敛到最优目标属性约简集。该算法能始终保持种群的多样性、协作性,并避免过早地陷入局部最优。相关仿真实验表明,AR-CPSO算法能有效地找到全局最优属性约简集,具有较强的属性协同约简优化性能。

关键词: 粒子群优化;向量分解;协同学习;属性约简;自适应罚函数

Abstract:

According to the problem of attribute reduction optimization, an improved cooperative PSO algorithm named AR-CPSO for attribute reduction optimization was proposed based on some special optimization advantages of PSO. In the process of searching for  the minimal attribute sets, particle swarms could improve its optimization ability by splitting  reduction vectors into some parts and learning some social cognition from cooperative neighbour clusters in the attribute spaces. The adaptive reinforcement penalty function method was involved in the algorithm to get the optimization reduction sets. AR-CPSO could maintain the diversity and cooperation of the populations. Furthermore, it could break away from the local optimization. Experimental results showed that AR-CPSO had an outstanding ability to find the global optimization and was better in cooperative attribute reduction.

Key words:  particle swarm optimization; vector split; cooperation learn; attribute reduction; adaptive penalty function

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