Multi-granularity Search Algorithm Based on Probability Statistics
ZHANG Qing-Hua1,2, GUO Yong-Long1, XUE Yu-Bin2
1.Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065 2.School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065
Abstract The basic idea of granular computing is to solve complicated problems in different granularity levels. To a great extent, the method indicates human intelligence in the solving process. Based on the human multi-granularity mechanism for solving complicated problems and the principle of probability statistics, an efficient multi-granularity search model based on statistical expectation is proposed from the viewpoint of granular computing. Then, the variety rule of the expectation in quotient spaces with different granularities is analyzed in detail. The experimental results demonstrate that with the granules divided into many sub-granules, the efficiency of the proposed method is gradually reduced and tends to be stable for searching a certain target. In addition, the complexity for solving the problem can be greatly reduced in different probability models.
2015, Vol. 28 
