一种求解属性约简优化的协同粒子群算法

Research of cooperative PSO for attribute reduction optimization

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。

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.