基于多蚁群的并行ACO算法

通过改变蚁群优化(ACO)算法行为，提出一种新的ACO并行化策略——并行多蚁群ACO算法。针对蚁群算法存在停滞现象的缺点，改进选择策略，实现具有自适应并行机制的选择和搜索策略，以加强其全局搜索能力。并行处理采用数据并行的手段，能减少处理器间的通信时间并获得更好的解。以对称TSP测试集为对象进行比较实验，结果表明，该算法相对于串行算法及现有的并行算法具有一定的优势。

Parallel ACO Algorithm Based on Multiple Ant Colony

This paper proposes and implements a new approach to parallel Ant Colony Optimization(ACO) algorithms by changing the behavior of ACO. In view of the shortcomings for ant algorithms' stagnant, by improving selection strategies, a new selection and search strategies with parallel adaptive mechanisms are implemented, so as to strengthen its global search capability, and the method of data parallel is used to reduce communication time between processors and get a better solution. The performance of the proposed parallel algorithm, applied to the Traveling Salesman Problem(TSP), is investigated and evaluated with respect to solution quality and computational effort. Experimental results demonstrate that the proposed algorithm outperforms the sequential ant colony system as well as the existing parallel ACO algorithms.