A minimum rough-set attribute reduction algorithm based on virus-coordinative discrete particle swarm
optimization(VCDPSO) is presented. In the algorithm, evolutions of the virus swarm are performed in coordination with the
particle swarm, and virus swarm keeps coordinative relations with the particle swarm by virus infection operations and best
virus seed extraction operation in order to improve the ability of local search of discrete particle swarm optimization(DPSO).
To enlarge the area of local search, the cut operator is introduced in the virus swarm’s self-renewal process. A proper fitness
function is defined and theoretical analysis and the experimental results on UCI dataset attribute reduction show that the
proposed method has better performance than other evolution attribute reduction algorithms, and the searching efficiency and
convergence rate for the global optimum are greatly improved as well.