动态调整路径选择的蚁群优化算法  

Ant Colony Optimization Algorithm with Path Choice of Dynamic Transition

作　　者：刘好斌[1] 胡小兵[1] 赵吉东[1] 

机构地区：[1]重庆大学数理学院,重庆400044

出　　处：《计算机工程》2010年第17期201-203,共3页Computer Engineering

基　　金：重庆市自然科学基金资助项目(CSPC;2005BB2197);重庆大学高层次人才科研启动基金资助项目(020800110420);重庆大学数理学院青年科研启动基金资助项目

摘　　要：针对蚁群算法收敛速度慢和存在停滞现象的缺点,提出对比度增强的路径选择规则以增强其全局搜索能力,选择规则加强了对反馈信息的利用,能加快算法的收敛速度,通过信息熵来动态控制对比度增强的方向,在避免算法停滞的同时加快了算法的收敛速度。将改进后的蚁群优化算法与传统的蚁群优化算法进行比较,仿真实验结果表明,改进算法具有较好的稳定性和全局优化性能,且收敛速度较快。Aiming at the disadvantage of slow convergence and stagnation phenomenon of ant colony algorithm, path choice rule is introduced, which is based on contrast intensification technology, in order to increase the probability of selecting solution components, path choice rule strengthens the use of feedback and speeds up convergence speed. Information entropy is used to dynamic control to direction of contrast enhancement, which avoid stagnation of the algorithm and speed up convergence. An example is given, which is simulated by using basic Ant Colony Optimization（ACO） and improved ACO. Simulation results show that the improved ACO has excellent stability, performance of global optimization, and fast convergence.

关 键 词：蚁群算法 路径选择 信息熵 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]