文[4]提出的多阶段输电网络规划并行蚁群算法(parallel ant colony algorithm，PACA)提高计算速度但存在算法早熟现象，容易陷入局部最优值。
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模式记忆并行蚁群算法 Schema recording parallel ant colony algorithm
Last Parallel ant colony algorithm proved to be feasible and effective by the experiments.
最后并行蚁群算法经实验证明可行、有效。
参考来源 - 半导体芯片制造调度并行蚁群算法研究At the same time,many ant colonies exist in parallel ant colony optimization.
在并行蚁群算法中,多个蚁群同时并行存在。
参考来源 - 离散群体智能算法的研究与应用·2,447,543篇论文数据，部分数据来源于NoteExpress
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.
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