摘要
针对基本蚁群算法收敛速度慢、易陷于局部最优从而导致搜索停滞的缺陷,提出了一种改进蚁群算法模型.改进算法引入信息素调节系数,避免算法初期各路径上信息素出现过大差异,导致算法"早熟".通过动态调整信息素挥发,在求解速度和寻找全局最优之间寻找平衡.对旅行商问题的仿真结果表明:改进算法的求解结果和求解效率都明显优于基本蚁群算法.
The basic ant colony algorithm converges slowly, is prone to plunge into partial optimum and results in search stagnation. In this paper, an improved ant colony algorithm is proposed. New algorithm introduces pheromone adjustment coefficient, and avoids appearing great differences in the paths early. By adjusting pheromone evaporation dynamically, balance is kept between solution speed and global optimum. The simulation results of traveling salesman problem show that improved algorithm is much more efficient than the basic ant colony algorithm.
出处
《数学的实践与认识》
CSCD
北大核心
2013年第22期157-161,共5页
Mathematics in Practice and Theory
基金
国家自然科学基金(50904032)
辽宁省教育厅科学技术研究项目(L2010177)
关键词
蚁群算法
局部最优
信息素
旅行商问题
ant colony algorithm（ACA）
partial optimum
pheromone
traveling salesman problem（TSP）
