Ant colony optimization has insufficient convergence and tends to fall into the local optima when solving the traveling salesman problem. This paper proposes a co-evolutionary multi-colony ant colony optimization (MCGACO) to overcome this deficiency and applies it to the Robot Path Planning. First, a dynamic grouping cooperation algorithm, combined with Ant Colony System and Max-Min Ant System, is introduced to form a heterogeneous multi-population structure. Each population co-evolves and complements each other to improve the overall optimization performance. Second, an adaptive guidance mechanism is proposed to accelerate convergence speed. The mechanism includes two parts: One is a dynamic evaluation network, which is used to evaluate and divide all solutions by the evaluation function. The other is a positive-negative incentive strategy, which can enhance the guiding role of solutions with higher evaluation value. Besides, to jump out of the local optima, an inter-specific co-evolution mechanism based on the game model is proposed. By dynamically determining the optimal communication combination, the diversity among populations can be well balanced. Finally, the experimental results demonstrate that MCGACO outperforms in terms of solution accuracy and convergence. Meanwhile, the proposed algorithm is also feasible for application in Robot Path Planning.

蚁群优化在解决旅行商问题时收敛性不足，容易陷入局部最优。本文提出了一种协同进化的多群体蚁群优化（MCGACO）来克服这一缺陷并将其应用于机器人路径规划。首先，引入动态分组协作算法，结合蚁群系统和最大-最小蚂蚁系统，形成异构的多种群结构。每个种群共同进化并相互补充以提高整体优化性能。其次，提出了一种自适应引导机制来加快收敛速度​​。该机制包括两部分：一是动态评价网络，用于通过评价函数对所有解进行评价和划分。另一种是正负激励策略，可以增强具有较高评价价值的解决方案的指导作用。此外，为了跳出局部最优，提出了一种基于博弈模型的种间协同进化机制。通过动态确定最优通信组合，可以很好地平衡种群间的多样性。最后，实验结果表明 MCGACO 在求解精度和收敛性方面优于其他算法。同时，该算法在机器人路径规划中的应用也是可行的。实验结果表明，MCGACO 在解决方案的准确性和收敛性方面表现出色。同时，该算法在机器人路径规划中的应用也是可行的。实验结果表明，MCGACO 在解决方案的准确性和收敛性方面表现出色。同时，该算法在机器人路径规划中的应用也是可行的。