Research on Transportation Planning based on Improved Wolf Pack Algorithm for Disaster Rescue

Addressing the challenge of optimizing the transportation efficiency of materials and personnel in major disaster emergency rescue, an improved wolf pack algorithm (IWPA) integrating a genetic algorithm (GA) and a simulated annealing (SA) mechanism is introduced. Firstly, the crossover-mutation mechanism of GA and the probabilistic jumping characteristics of SA are incorporated into the wolf pack algorithm framework, effectively enhancing the algorithm’s global search capability and convergence speed. Subsequently, a multi-objective optimization model considering path planning, vehicle scheduling, and time-resource constraints is established for rescue operations. Finally, simulation experiments designed for typical disaster rescue transportation scenarios, combined with Ackley benchmark function testing demonstrate that IWPA exhibits rapid global convergence speed, provides new technical support for emergency rescue operations, and holds application value for enhancing the efficiency of emergency rescue in major disasters.