摘要
针对基本蚁群算法的搜索时间长和局部收敛等现象,提出一种用于求解旅行商问题(TSP)的优化型蚁群算法,该算法有效地将最大最小蚁群算法(MMAS)和遗传算法(GA)相结合,一方面在很大程度上缩短了算法的寻优时间;另一方面有效地避免了算法的早熟停滞现象。利用MATLAB对多种TSP问题进行仿真研究,实验结果证明了优化型蚁群算法在性能上优于MMAS和GA。
Aiming at the phenomena such as searching for a long time and the local convergence of ant colony algorithm,this paper presents a new optimization ant colony algorithm to solve traveling salesman problem.It effectively ant colony algorithm and genetic algorithm combined,on the one hand a large extent,the algorithm optimization to shorten the time;the other hand,the algorithm was effective in avoiding premature stagnation.Using matlab simulation of the TSP,the experiment proves the algorithm is better than MMAS and GA.
出处
《计算机与数字工程》
2010年第6期22-25,共4页
Computer & Digital Engineering
关键词
最大最小蚁群算法
信息素
旅行商问题
遗传算法
max-min ant system
pheromone
traveling salesman problems
genetic algorithm
