本文根据影响并行蚁群算法性能的关键因素，提出了一种自适应的并行蚁群算法。
An adaptive parallel ant colony algorithm is presented by considering the critical factors influencing the parallelization of the ant colony algorithm.
通过实验证明，改进后的并行蚁群算法程序执行时间明显缩短，执行效率显著提高。
The experiment proves that the improved method makes the time of program execution shorter significantly and the efficiency higher observably when solve large scale TSP.
为求解该模型，并综合考虑优化质量和通信开销，采用了基于粗粒度模型的并行蚁群算法。
Considering the communication cost and optimization qualities, an ant colony algorithm based on the coarse-grain model is designed to solve the problem of this model.
蚁群算法采用分布式并行计算机制，易与其他方法结合，具有较强的鲁棒性等特点。
ACO adopts parallel computation mechanism, has strong robustness and is easy to combine with other methods in optimization.
蚁群算法采用分布式并行计算机制，易与其他方法结合，具有较强的鲁棒性等特点。
ACO adopts parallel computation mechanism, has strong robustness and is easy to combine with other methods in optimization.
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