An Improved Iterated Greedy Algorithm for Solving Rescue Robot Path Planning Problem with Limited Survival Time

Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario. In this study, we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem (TSP) with life-strength constraints. To address this problem, we proposed an improved iterated greedy (IIG) algorithm. First, a push-forward insertion heuristic (PFIH) strategy was employed to generate a high-quality initial solution. Second, a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability. Furthermore, three problem-specific swap operators were developed to improve the algorithm’s exploitation ability. Additionally, an improved simulated annealing (SA) strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima. To verify the effectiveness of the proposed algorithm, the Solomon dataset was extended to generate 27 instances for simulation. Finally, the proposed IIG was compared with five state-of-the-art algorithms. The parameter analysis was conducted using the design of experiments (DOE) Taguchi method, and the effectiveness analysis of each component has been verified one by one. Simulation results indicate that IIG outperforms the compared algorithms in terms of the number of rescue survivors and convergence speed, proving the effectiveness of the proposed algorithm.