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

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.