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首页> 外文会议>2012 International Conference on Robotics and Biomimetics : Conference Digest. >An improved ant colony optimization algorithm based on dynamically adjusting ant number
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An improved ant colony optimization algorithm based on dynamically adjusting ant number

机译：一种基于动态调整蚂蚁数量的改进蚁群优化算法

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摘要

The ant colony algorithm is a mature and effective method to solve the problem of optimizing shortest path, which is one of the key technologies for robot navigation and path planning. But the algorithm often fails into precocity easily and can't get the global best result. This paper proposes an improved ant colony optimization algorithm by dynamically adjusting ant number. The main idea of this algorithm is that only the part of the ants passing the shorter path is allowed to release pheromone and update the total ant number randomly or fixedly in algorithm iterative process. So, the improved algorithm can increase the randomness in the search and improve global search ability. To verify the performance of this algorithm, this paper uses the improved algorithm to solve Chinese Traveling Salesmen Problem. The simulation results show that compared with the traditional ant colony algorithm, the improved ant colony algorithm is easier to find the optimal solution, and its optimization ability is stronger.
机译：蚁群算法是解决最短路径优化问题的一种成熟有效的方法，它是机器人导航和路径规划的关键技术之一。但是该算法常常容易陷入早熟，无法获得全局最佳结果。通过动态调整蚂蚁数量，提出了一种改进的蚁群优化算法。该算法的主要思想是在算法迭代过程中，仅允许经过较短路径的那部分蚂蚁释放信息素并随机或固定更新总蚂蚁数。因此，改进的算法可以增加搜索的随机性，提高全局搜索能力。为了验证该算法的性能，本文使用改进算法来解决中国旅行商问题。仿真结果表明，与传统的蚁群算法相比，改进后的蚁群算法更容易找到最优解，优化能力更强。

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