基于可变天气因素的MMAS改进算法  

Improved MMAS algorithm based on variable weather factors

作　　者：焦宗浩 高绍姝 李克文 JIAO Zong-hao;GAO Shao-shu;LI Ke-wen(College of Computer and Communication Engineering,China University of Petroleum,Qingdao 266580,China)

机构地区：[1]中国石油大学计算机与通信工程学院,山东青岛266580

出　　处：《计算机工程与设计》2020年第4期952-957,共6页Computer Engineering and Design

基　　金：国家自然科学基金项目(61801517)。

摘　　要：针对最大最小蚂蚁系统(MMAS)容易导致算法快速陷入局部最优的问题,提出一种基于可变天气因素的MMAS改进算法(variable weather MAX-MIN ant system,VW-MMAS)。通过由天气变化影响信息素的变化来改善MMAS的寻优过程,具体引入以下机制:在信息素挥发机制方面,参考天气变化因素对蚂蚁觅食的影响,设置信息素挥发系数和蚁群数量;在算法陷入局部最优时,综合考虑TSP问题中城市间的距离,增强不是最优路径的信息素,扩大蚂蚁的搜索范围。应用该算法解决TSP问题,将仿真结果与其它算法进行比较,验证了该算法的有效性,提高了解的质量。To solve the problem that the MAX-MIN ant system(MMAS)is easy to fall into the local optimum,an improved MMAS algorithm based on variable weather factor(VW-MMAS)was proposed.The algorithm improved the optimization process of MMAS by affecting pheromone change by weather change.In the pheromone volatilization mechanism,pheromone volatilization coefficients and ant colony numbers were set according to the influence of weather change on ant foraging.When the algorithm fell into local optimum,the distance between cities of TSP problem was considered comprehensively to enhance pheromones of paths which were not optimal,and the search scope of ants was further expanded.The algorithm was applied to solve TSP problem,and the simulation results were compared with other algorithms,which verified the effectiveness of the algorithm and improved the quality of the solution.

关 键 词：最大最小蚂蚁系统 可变天气因素 信息素 信息素挥发系数 旅行商问题 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]