关键词: 粒子群, 粗糙集, 属性约简, 支付效用矩阵, 博弈策略

Abstract: This paper establishes relationship between particle swarm optimization algorithms and game theory, on the basis of which a swarm intelligence based search mechanism is proposed and applied to solving the attribute reduction problem in the context of rough sets. The proposed attribute reduction algorithm can set up different participatory groups and game strategies, construct corresponding pay utility matrix, and produce optimal combinations through gaming procedure. Numerical experiments on a number of UCI datasets show the proposed game strategies based reduction algorithm is superior to particle swarm optimization, tabu search, gene algorithm and PSO with mutation operator in terms of solution quality, and has lower computational cost.

Key words: particle swarm optimization, rough set, attribute reduction, pay utility matrix, game strageties