Multi-point shortest path planning problem is a typical problems in discrete optimization. The bat algorithm is a nature-inspired metaheuristic optimization algorithm that is used in a wide range of fields. However, there is one problem with the BA, which is easy to premature. To solve multi-point shortest path planning problem, an improved discrete bat algorithm (IDBA) is proposed in this paper. In this algorithm, the Floyd–Warshall algorithm is first used to transform an incomplete connected graph into a complete graph whose vertex set consists of a start point and necessary points. Then the algorithm simulates the bats’ foraging and obstacle avoidance process to find the shortest path in the complete graph to satisfy the constraints. Finally, the path is transferred to the original incomplete graph to get the solution. In order to overcome the premature phenomenon of a discrete bat algorithm, the modified neighborhood operator is proposed. To prove the effectiveness of our method, we compared its performance in 26 instances with the results obtained by three different algorithms: DBA, IBA and GSA-ACS-PSOT. We also performed a sensitivity analysis on the parameters. The results indicate that the improved bat algorithm outperforms all the other alternatives in most cases.

多点最短路径规划问题是离散优化中的典型问题。bat算法是一种自然启发式的元启发式优化算法，广泛应用于各个领域。但是，BA存在一个问题，该问题很容易过早出现。为解决多点最短路径规划问题，提出了一种改进的离散蝙蝠算法（IDBA）。在此算法中，首先使用Floyd–Warshall算法将不完整的连接图转换为完整的图，其顶点集由起点和必要点组成。然后该算法模拟蝙蝠的觅食和避障过程，以在完整图中找到最短路径来满足约束条件。最后，将路径转移到原始不完整图以获取解决方案。为了克服离散蝙蝠算法的过早现象，提出了改进的邻域算子。为了证明我们方法的有效性，我们将其在26个实例中的性能与通过三种不同算法（DBA，IBA和GSA-ACS-PSOT）获得的结果进行了比较。我们还对参数进行了敏感性分析。结果表明，在大多数情况下，改进的bat算法优于所有其他替代方法。