摘要
蚁群算法是一种新型的模拟进化算法,研究表明此算法具有一些优良性质,但是蚁群算法容易陷入局部最优。分析了蚁群算法陷入局部最优的主要原因,根据算法陷入最优的原因提出一种判断局部最优的方法;在蚁群算法中引入判断局部最优的策略,当算法陷入局部最优时对参数做相应的变化,来克服蚁群算法易陷入局部最优的缺陷。实验表明此方法行之有效。
Ant colony optimization （ACO） is a new kind of simulated evolutionary algorithm, and it has many good features, but it is easy to fall in local peak. The reason to fall in local peak was analyzed, and the method to escape from local peak was advanced. The result of the experiment suggests that the improved algorithm is effective.
出处
《计算机工程与设计》
CSCD
北大核心
2005年第11期3065-3066,3114,共3页
Computer Engineering and Design
