基于模式求解旅行商问题的蚁群算法

Ant Colony Algorithm Based on Model Algorithm for Traveling Salesman Problem

群体智能已经被广泛应用于分布式控制、调度、优化等领域.其中蚁群算法已经成为该领域的一个研究热点.在蚁群算法的基础上针对旅行商问题(TSP),首先提出了小窗口蚁群算法,提高初始解的质量,然后与基于模式的蚁群算法相结合,通过提取模式,改变计算粒度,缩短计算时间,提高计算精度.实验结果表明该算法有较好的效果.

Swarm intelligence has been applied in domains of distributed control, job-shop schedule, and optimization. Ant colony algorithm(ACO),one of swarm intelligence, has become a hot research field. This paper proposes an ant colony algorithm based on little window and obtains models from typical ant algorithm. The algorithm reduces computing time and improves computing accuracy by limiting the size of solution space, extracting models and changing computing granularity. Simulations demonstrate that the improved algorithm can achieve better performance than typical algorithm and some other improved algorithms.