求解复杂优化问题的基于信息熵的自适应蚁群算法  

Adaptive Ant Colony Algorithm Based on Information Entropy for Complicated Optimization Problems

作　　者：李万庆[1] 李彦苍[2] 

机构地区：[1]天津大学管理学院,天津300072 [2]河北工程学院,河北邯郸056038

出　　处：《数学的实践与认识》2005年第2期134-139,共6页Mathematics in Practice and Theory

基　　金：国家自然科学基金 (60 0 75 0 1 3 )

摘　　要：针对基本蚁群算法存在收敛速度慢、易陷入局部最优、计算复杂且不易求解连续优化问题等缺陷 ,提出了一种基于信息熵的改进自适应蚁群算法 ,采用由信息熵控制的路径选择及随机扰动策略实现了算法的自适应调节 ,克服了基本蚁群算法的不足 .典型的 NP-hard问题的计算实例表明 ,该方法具有较好的收敛性、稳定性和鲁棒性 ,可用于离散及连续的组合优化问题求解中 ,其不失为求解复杂组合优化问题的一种较好的方法 .Ant colony algorithm has much deficiency, specially it is easy to fall into the local best,its calculation is complicated and its shortcoming in solving the continual-space problem. According to this, an improved ant colony algorithm based on the information entropy is proposed in the paper. The information entropy is used to control the path selection and evolutional strategy by self-adjusting. Application results in solving the NP-hard problems have shown its efficiency and robustness in solving the combinatorial optimization problems.

关 键 词：求解 连续 组合优化问题 蚁群算法 收敛性 收敛速度 信息熵 自适应 鲁棒性 局部最优 

分 类 号：O241[理学—计算数学]