Multi-point shortest path planning based on an Improved Discrete Bat Algorithm

Abstract 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.