摘要
介绍蚁群算法结构、原理,分析其优点和不足,回顾它的几个重要的改进模型.为了改进它的不足.在最大最小蚂蚁系统的基础上,提出一种自适应改进模型.对其权重系数、状态转移规则及信息素增量方式等进行改进,实现自适应调整,提高算法性能.为了验证改进算法的性能,进行数值实验,结果显示本文所提改进算法的有效性.
The structure and principle of ant colony algorithm are introduced. Its excellence and deficiency are analyzed, and several important improved models are reviewed. Based on Max-Min Ant Systems （MMAS）, an adaptive improved model is put forward. To achieve adaptive adjustment of parameters and enhance the performance of the proposed algorithm, the weighting coefficient, state transferring rule and pheromone increment mode are improved. To testify the performance of the improved algorithm, numerical experiment is made and the result shows the improved algorithm is effective.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2007年第5期688-691,共4页
Pattern Recognition and Artificial Intelligence
作者简介
苏畅，女，1981年生，硕士，主要研究方向为智能控制．E-mail：tovegar@yahoo．com．cn．
徒君，男，1982年生，硕士，主要研究方向为智能算法．
