基于云模型的模糊自适应蚁群算法研究  

作者:李絮;刘争艳 刊名:《计算机工程与应用》 上传者:李岚

【摘要】针对蚁群算法存在控制参数难以确定和易陷入停滞等不足,采用云模型理论对蚁群算法进行改进,将云模型作为模糊隶属函数,选择部分较优路径进行全局信息素更新,从而提高算法对路径的开发和探索,同时通过对云隶属函数的参数控制,实现算法的自适应调整策略。针对TSP问题进行仿真实验对比,结果也表明基于云模型的蚁群算法要明显优于ACS和MMAS算法。

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24 2016,52(2) Computer Engineering and Applications计算机工程与应用 基于云模型的模糊 自适应蚁群算法研究 李 絮,刘争艳 LI Xu,LIU Zhengyan 阜阳师范学院 计算机与信息学院,安徽 阜阳 236041 School of Computer and Information,Fuyang Teachers College,Fuyang,Anhui 23 604 1,China LI Xu,LIU Zhengyan.Fuzzy self-adaptive ant colony algorithm based on cloud mode1.Computer Engineering and Applications,2016,52(2):24-27. Abstract:Since the contro1 parameter 1S dif cult to determ ine and the algorithm iS easy to fall 1nto stagnation.SO there are still deficiencies in the ant colony algorithm.In this paper,the cloud model theory is adopted to improve the ant colony algorithm and a novel ant colony algorithm is proposed.In order to improve the algorithm’S ability to develop and explore for path,the cloud model is used as the fuzzy membership function and the some global better paths are selected to update the pheromone.Meanwhile,by using the parameter of cloud membership function,the proposed algorithm can achieve self-adaptive mechanism.Simulation experimental results for the TSP show that the algorithm based on cloud model is more effective than both ACS and MMAS. Key words:ant colony algorithm;cloud model;fuzzy membership function;Traveling Salesman Problem(TSP) 摘 要:针对蚁群算法存在控制参数难以确定和易陷入停滞等不足 ,采用云模型理论对蚁群算法进行改进,将云模 型作为模糊隶属函数,选择部分较优路径进行全局信息素更新,从而提高算法对路径的开发和探索,同时通过对云 隶属函数的参数控制,实现算法的自适应调整策略。针对TSP问题进行仿真实验对比,结果也表明基于云模型的蚁 群算法要明显优于Acs和MMAs算法。 关键词:蚁群算法;云模型;模糊隶属函数;旅行商问题(TSP) 文献标志码:A 中图分类号:TP391 doi:10.3778/j.issn.1002.8331.1401.01

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