摘要
融合遗传算法和蚂蚁算法,解决了遗传算法由于冗余迭代而导致的压缩效率低和蚂蚁算法由于初信息素匮乏而导致的算法速度慢的缺点.在传统优化算法的基础上引入了多目标的思想,并对蚂蚁算法中信息素进行了改进,将其用于空间数据的矢量数据压缩,在同时满足压缩率高和误差小两个目标时,取得了良好的压缩效果.
Combining genetic algorithm and ant colony system,this paper solved the weaknesses both in low efficiency compressibility by redundancy iteration of genetic algorithm and low speed by pheromone deficient of ant colony system.The paper introduced multi-object into traditional optimization algorithm and improves the pheromone in Ant Colony System.After applying it to the vector data compression of spatial data,good result has been obtained with high compression rate and low discrepancy.
出处
《西安文理学院学报（自然科学版）》
2009年第4期69-72,共4页
Journal of Xi’an University(Natural Science Edition)
关键词
遗传算法
蚂蚁算法
空间数据
矢量数据压缩
信息素
genetic algorithm
ant colony system
spatial data
vector data compression
pheromone
