【摘要】 针对传统蚁群算法初期容易盲目搜索，收敛速度慢，全局性不强，搜索路径安全性不足等问题，提出了一种改进蚁群算法。通过全局变量改进启发函数，使算法不容易陷入局部最优；用椭圆几何法构造数学模型，来重新分配初始信息素，使算法初期不会盲目搜索；提出自适应有界的信息素迭代方式，增强收敛速度。除距离最优，时间最优外，提出安全最优的邻接矩阵改造方法，避免机器人移动中的磕碰现象。仿真结果表明，改进后算法收敛速度提高，迭代次数减少，初期搜索效率加强，规划路径避开所有障碍物顶点，验证了算法的有效性，优越性和安全性。更多还原

【Abstract】 Aiming at the problems that the traditional ant colony algorithm is easy to search blindly in the initial stage, the convergence speed is slow, the globality is not strong, and the security of the search path is insufficient, an improved ant colony algorithm is proposed. The heuristic function was improved by global variables, so that the algorithm was not easy to fall into local optimum; The mathematical model was constructed by elliptic geometry method to redistribute the initial pheromone, so that the algorithm would not search blindly in the initial stage; An adaptive bounded pheromone iteration method was proposed to enhance convergence speed. In addition to the optimal distance and time, a safe and optimal adjacency matrix transformation method was proposed to avoid the collision phenomenon in the movement of the robot. The simulation results show that the improved algorithm improves the convergence speed, reduces the number of iterations, enhances the initial search efficiency, and avoids all obstacle vertices by planning the path, which verifies the effectiveness, superiority and security of the algorithm.更多还原