Reduction Methods of Attributes based on Binary Particle Swam with Simulated Annealing
This paper proposed a binary particle swam optimization method based on simulated annealing. The simulated annealing was introduced when particles updated their position. The algorithm convergence was controlled by adjusting the speed of annealing. The particles would not easily jump out of the expected search area when the fall of temperature was slow enough, which improved the particles local search capability and made the optimization algorithm more efficient This algorithm was applied to the attribute reduction of casing damage prediction attributes were reduced from original 62 to 12. The complexity of aftermath processing was significantly reduced.
Artificial Intelligence Attribute Reduction BPSO Simulated Annealing
Pan Guanyu Yan Hui
Department of Information Engineering, Jilin Business and Technology College, Changchun 130062, China
国际会议
重庆
英文
1102-1104
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）