分组教学蚁群算法改进及其在机器人路径规划中应用

针对蚁群算法收敛速度慢、易陷入局部最优问题，提出一种基于分组教学优化改进蚁群算法。该算法从3个角度对蚁群算法进行改进。首先，利用分组教学优化算法改进蚁群算法适应度函数，提高算法全局求解能力。同时，引进一种新的回退策略，通过该策略处理U型障碍死锁问题，确保算法求解可行性。其次，采用一种新的动态信息素更新策略，滚动更新每轮迭代后路径信息素值，避免算法陷入局部最优。最后，引入路径简化算子，将冗余角简化为直线路径，缩短路径长度。仿真实验证明改进算法能有效提高移动机器人路径规划收敛速度和精度。

Improvement of ant colony algorithm in group teaching and its application in robot path planning

To solve the problems of slow convergence speed and easily falling into local optimization, a novel improved ant colony algorithm is proposed based on a group teaching optimal algorithm (GTACO). The improved ant colony algorithm is optimized in three aspects. Firstly, the group teaching optimization algorithm is used to improve the fitness function of the ant colony algorithm to enhance the searching ability of global solutions. Simultaneously, a new fallback strategy is designed to deal with the U-shaped deadlock and ensure the feasibility of the solution. Secondly, a novel updating strategy for dynamic pheromones is adopted to avoid falling into local optimization of the algorithm by updating the path pheromone value after each iteration. Finally, the simplification operator of the path is applied to shorten the length of the path by simplifying the redundant corners into linear paths. Simulation experiments show that the improved algorithm can effectively increase the ability of path planning in convergence speed and accuracy for mobile robots.