基于改进小生境遗传算法的三角模糊数互补判断矩阵排序方法  

Priority method of triangular fuzzy number complementary judgment matrix based on improved niche genetic algorithm

作　　者：杨雪康 匡兵[1] 林瑞[1] 周峰[1] 

机构地区：[1]桂林电子科技大学机电工程学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2017年第3期254-258,共5页Journal of Guilin University of Electronic Technology

基　　金：国家自然科学基金(51265006)

摘　　要：针对三角模糊数互补判断矩阵(triangular fuzzy number complementary judgment matrix,简称TFN)排序中运算耗时较长的问题,提出一种基于改进小生境遗传算法(niche genetic algorithm,简称NGA)的TFN排序方法。将TFN排序中满意一致性检验、矩阵元素调整和权值排序作为目标建立优化模型,在保证TFN具有满意一致性的前提下,对TFN进行最小调整。通过引入度量种群多样性的小生境熵概念对NGA进行改进,根据小生境熵对改进NGA中部分进化参数进行自适应调整,提高算法的运算效率。采用该方法对随机产生的3~6阶TFN进行仿真验算,仿真结果表明,采用改进NGA提高了运算效率,改善了运算稳定性。A TFN sorting method based on improved niche genetic algorithm （NGA） is proposed to solve the problem of long computation time in TFN （triangular fuzzy number complementary judgment matrix）. The TFN ranking method is used to set up the optimization model to satisfy the consistency test, the matrix element adjustment and the weight order as the opti- mization target, and to minimize the initial judgment matrix under the condition that the TFN has satisfactory consistency. The NGA is improved by introducing the concept of niche entropy to measure the population diversity. The part of the evo- lutionary parameters in the improved NGA is adjusted adaptively according to the niche entropy, and the efficiency of the al gorithm is improved. The simulation results show that the improved NGA can improve the operation efficiency and the sta- bility of computation.

关 键 词：三角模糊数互补判断矩阵 改进小生境遗传算法 运算效率 

分 类 号：N945.25[自然科学总论—系统科学]