A LODBO algorithm for multi-UAV search and rescue path planning in disaster areas

In disaster relief operations, multiple UAVs can be used to search for trapped people. In recent years, many researchers have proposed machine le arning-based algorithms, sampling-based algorithms, and heuristic algorithms to solve the problem of multi-UAV path planning. The Dung Beetle Optimization (DBO) algorithm has been widely applied due to its diverse search patterns in the above algorithms. However, the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic, potentially leading to an inability to fully explore the search space and a tendency to converge to local optima, thereby not guaranteeing the discovery of the optimal path. To address these issues, we propose an improved DBO algorithm guided by the Landmark Operator (LODBO). Specifically, we first use tent mapping to update the population strategy, which enables the algorithm to generate initial solutions with enhanced diversity within the search space. Second, we expand the search range of the rolling ball dung beetle by using the landmark factor. Finally, by using the adaptive factor that changes with the number of iterations., we improve the global search ability of the stealing dung beetle, making it more likely to escape from local optima. To verify the effectiveness of the proposed method, extensive simulation experiments are conducted, and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm (GA), the Gray Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA) and the original DBO algorithm in the disaster search and rescue task set.