摘要： 为避免蚁群优化算法容易早熟的缺点，在转移概率公式中引入一个新的自适应因子。随着迭代次数的增加，该因子有利于蚂蚁探索有较弱信息素浓度的边而避免一些边上信息素的过度积累。该特点使蚂蚁在迭代后期仍能以较高概率搜索到更好的解从而避免早熟。仿真实验结果表明，该算法对解决旅行商问题具有更优的全局搜索能力。
关键词:
蚁群优化,
自适应转移概率,
旅行商问题
Abstract: A new factor in transition rule is employed to overcome the premature behavior in Ant Colony Optimization(ACO). The factor can help the ants to obtain a better result by exploring the arc with low pheromone trail accumulated so far as time elapses. Besides, it can avoid the overconcentration of pheromone trail to enlarge the searching range. Simulation results show that the Improved Ant Colony System(IACS) has better performance in solving Traveling Salesman Problem(TSP) and more outstanding global optimization properties.
Key words:
Ant Colony Optimization(ACO),
adaptive transition probability,
Traveling Salesman Problem(TSP)
中图分类号:
何雪海, 胡小兵, 赵吉东, 王志. 基于自适应转移概率的蚁群优化算法[J]. 计算机工程, 2010, 36(23): 165-167.
HE Xue-Hai, HU Xiao-Bing, DIAO Ji-Dong, WANG Zhi. Ant Colony Optimization Algorithm
Based on Adaptive Transition Probability[J]. Computer Engineering, 2010, 36(23): 165-167.