基于二进制粒子群优化的一个最小属性约简算法  

Minimum Attribute Reduction Algorithm Based on Binary Particle Swarm Optimization

作　　者：叶东毅[1] 廖建坤[1] 

机构地区：[1]福州大学数学与计算机科学学院,福州350002

出　　处：《模式识别与人工智能》2007年第3期295-300,共6页Pattern Recognition and Artificial Intelligence

基　　金：福建省自然科学基金项目(No.2006J0029);福建省高新科技研究开发重点项目(No.2005H028)资助

摘　　要：研究基于二进制粒子群优化算法思想求解决策表最小属性约简问题的方法.定义适当的适应值函数,将决策表最小属性约简问题转化为一个适合二进制粒子群优化算法求解的0—1组合优化问题,证明问题解的等价性.在此基础上,引入种子粒子概念及其自适应保护策略,提出一个改进的二进制粒子群算法,取得良好的效果.实验结果说明该算法的有效性.Based on binary particle swarm optimization, a minimum attribute reduction algorithm for a decision table is presented. A proper fitness function is defined. Thus , the minimum attribute reduction problem is equivalently transformed into a binary combinatorial optimization problem without additional nonlinear constraints. The concept of a seed particle is introduced with its protection strategy. Finally, an improved binary particle swarm optimization algorithm is proposed to solve the transformed problem. Experimental results show the effectiveness of the presented algorithm.

关 键 词：最小属性约简 适应值函数 二进制粒子群优化 种子粒子保护 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]