摘要
原有的遗传融合蚁群算法虽然克服了基本蚁群算法的不足,优化效果得到了改善,能够克服收敛速度较慢,易出现停滞,以及全局搜索能力较低的缺陷。但是还不够,因此,在原有的遗传融合蚁群算法的基础上进行了许多改进以扩大解的搜索空间,更加提高其全局优化寻优速度。并将遗传融合蚁群算法和改进的遗传融合蚁群算法分别应用于TSPLIB中的Att532TSP问题进行了仿真实验。仿真研究表明,改进后的算法具有更优良的全局优化性能,效果令人满意。
Compared to basic ant colony algorithm, former ant colony algorithm based on genetic gene has overcome lots of prob- lems,such as slow convergence speed,easy to get stagnated,and low ability of full search etc.But it is still not enough.So,a new ant colony algorithm based on genetic gene is proposed,which can enlarge solution search space,and improve the speed of global optimization.The simulation of applying these two methods to solve Att532TSP problem of TSPLIB has been done separately.The result of simulation shows that the new algorithm has better global optimization ability.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第4期43-45,共3页
Computer Engineering and Applications
基金
湖南省自然科学基金No.06JJ5116
湖南省教育厅自然科学基金No.05C408~~
关键词
遗传算法
蚁群算法
信息素
仿真
genetic algorithm
ant colony algorithm
information element
simulation
