Authors: Yan-ping Bai; Yu Zhang
Addresses: School of Management, Capital Normal University, Beijing, 100048, China ' State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing, 100083, China; Department of Computer Teaching and Network Information, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China
Abstract: In this paper, we established a relationship between particle swarm optimisation algorithms and game theory. On that basis, 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 optimisation, tabu search, gene algorithm and PSO with mutation operator in terms of solution quality, and has lower computational cost.
Keywords: swarm intelligence; rough sets; pay utility matrix; game theory; computing science; particle swarm optimisation; PSO; attribute reduction.
International Journal of Computing Science and Mathematics, 2013 Vol.4 No.3, pp.287 - 297
Received: 13 May 2013
Accepted: 26 Jun 2013
Published online: 10 May 2014 *