Minimum attribute reduction is the optimum problem in the attribute reduction of the rough sets theory.To seek the minimum attribute reduction

the attribute reduction algorithm based on the particle swarm optimization (ARPSO algorithm)beats the traditional attribute reduction algorithm.In existed ARPSO algorithms

the positive region is usually taken as the heuristic information

however

it is not precision enough to measure the uncertainty.The mutual information is a more efficient tool to measure the uncertainty in the rough sets theory.To handle this problem

an attribute reduction algorithm based on the particle swarm optimization takes the mutual information(MIPSO algorithm)as a term in the fitness function

The proposed MIPSO algorithm improves the regional shock search embedded particle swarm optimization algorithm(RSPSO)by enhancing the speed which the particle is close to the attractor

preventing from being local optimum early and finding the optimum as soon as possible.Consequently

the global convergence of the MIPSO algorithm is guaranteed as soon as possible.The experimental results show that the proposed MIPSO algorithm not only improves the optimization ability

accelerates the speed and improves the accuracy

but also can keep the mutual information value of all attributes before reducing approximately equal to the value of remaining attributes after reducing.