中文摘要：

蚁群算法求解函数问题，人工蚂蚁的搜索范围和信息素浓度更新速度直接影响到是否能够获得全域最优解。为了获得更加稳定且准确的全域最优解，受自然蚂蚁觅食后返巢行为的启发，提出了具有觅食-返巢机制的蚁群算法。该算法主要通过增大人工蚂蚁的搜索范围以及加快信息素浓度的更新速度进行改进。通过函数测试，结果表明：觅食-返巢连续域蚁群算法相比于以往的遗传算法和连续域蚁群算法，能够得到更好的计算结果和运行时间。因此觅食-返巢机制使得蚁群算法求解全域最优解的能力获得了提高。

英文摘要：

The optimal solution of ant colony algorithm solving continuous function is impacted by searching range and pheromone update rate of artificial ants. For stable and accurate optimal solution, ant colony algorithm with foraging-homing mechanism is presented, which is inspired by foraging-homing behaviors of ant colonies. Searching？range is expanded and？pheromone update rate is accelerated. According to function test, results show that：？ant colony algorithm with foraging-homing mechanism can get better optimal solution and computing time compared to former genetic algorithm and ant colony algorithm. Therefore foraging-homing mechanism improves the ant colony algorithm.