Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy

doi:10.3778/j.issn.1673-9418.2008060

Abstract

Abstract:

Aiming at the problem that the ant colony algorithm has slow convergence rate and poor diversity in solving traveling salesman problem (TSP), double ant colony algorithm based on collaborative mechanism and dynamic regulation strategy is proposed. Firstly, the ant colony is dynamically divided into guide ants and cooperative ants according to fitness value, so as to form a heterogeneous double ant colony. Secondly, the heterogeneous double ant colony adopts the collaborative mechanism to balance the diversity and convergence rate of the algorithm: the guide ant introduces the propagation factor in the path construction, which increases the probability of the ant choosing a new path, expands the search range, and improves the diversity of the algorithm. The cooperative ant is guided by the optimal path of the guide ant. When the path similarity reaches the threshold, the cooperative operator is started to accelerate the convergence speed. Finally, the dynamic regulation strategy is introduced, the adaptive control operator is introduced when the global pheromone is updated, and the pheromone of the global optimal path is positively stimulated or reverse-penalized, so as to accelerate the convergence speed and avoid the algorithm falling into the local optimal. The experimental results of solving the TSP test set show that the improved algorithm not only improves the quality of solutions, ensures the diversity of algorithms, but also speeds up the convergence speed of the algorithm, especially in large-scale urban problems.

Key words: ant colony algorithm, propagation factor, cooperation operator, collaborative mechanism, dynamic regulation strategy, traveling salesman problem (TSP)

摘要：

针对蚁群算法在求解旅行商问题（TSP）时出现的收敛速度慢和多样性较差的问题，提出结合协同机制与动态调控策略的双蚁群算法。首先，将蚁群根据适应度值动态地划分为导向蚁和合作蚁，从而构成异构双蚁群。其次，异构双蚁群采用协同机制平衡算法多样性和收敛速度：导向蚁在路径构建时引入传播因子，增大蚂蚁选择新路径的概率，扩大搜索范围，提高算法多样性；合作蚁受导向蚁中最优路径的引导，当路径相似度达到阈值时，启动合作算子，加快算法收敛速度。最后，引入动态调控策略，在全局信息素更新时引入自适应调控算子，对全局最优路径的信息素进行正向激励或反向惩戒，加快收敛速度的同时避免算法陷入局部最优。求解TSP测试集的实验结果表明，该算法不仅提高了解的质量，保证了算法多样性，而且加快了算法收敛速度，尤其在大规模城市问题中效果更为明显。

关键词: 蚁群算法, 传播因子, 合作算子, 协同机制, 动态调控策略, 旅行商问题（TSP）

MENG Jingwen, YOU Xiaoming, LIU Sheng. Double Ant Colony Algorithm Based on Collaborative Mechanism and Dynamic Regulation Strategy[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(11): 2206-2221.

孟静雯，游晓明，刘升. 结合协同机制与动态调控策略的双蚁群算法[J]. 计算机科学与探索, 2021, 15(11): 2206-2221.

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