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