【摘要】 针对传统蚁群算法在二维栅格地图下存在搜索时间长、收敛速度慢、考虑因素单一等问题，提出了一种融合蚁群-A~*算法。首先将启发式方法的思想融入到蚁群算法，优化蚁群算法的搜索效率；其次引入最大最小蚂蚁系统，提出一种精英蚂蚁信息素更新规则；同时增加考虑转向次数、转向角度等因素，在启发式信息中加入弯曲抑制算子，减少弯曲次数和累积弯曲角度，避免算法以优化路径长度作为单一目标；最后提出一种改进撤回机制，解决算法死锁问题。仿真表明，在相同地图环境中，改进的蚁群算法在路径长度、路径拐点以及收敛速度都有了显著提升，更适用于复杂环境。更多还原

【Abstract】 Traditional ant colony algorithm has problems such as long search time, slow convergence speed, and single consideration factor in two-dimensional grid maps.A fusion algorithm of ant colony and A~* was proposed to address the problems. First, the idea of heuristic methods was integrated into ant colony algorithm to optimize its search efficiency.Second, the max-min ant system was introduced to propose an elite ant pheromone updating rule.Meanwhile, the factors such as number of turns and turning angle were considered to increase a bending suppression operator in the heuristic information to reduce the number of bends and cumulative bending angles, avoiding the algorithm that optimize path length as a single objective. Finally, an improved recall mechanism was propose to address algorithm deadlock issues. Experiments show that in the same map environment, the improved ant colony algorithm significantly reduces path length, path inflection points, and accelerates convergence speed, making it more suitable for complex environments.更多还原