Exponential Rank Differential Evolution Algorithm for Disaster Emergency Vehicle Path Planning

When disasters happen, victims suffer from lots of miseries. Vehicles are used to carry relief supplies for these victims. However, these vehicles are subject to threats as roads and bridges are often destroyed by these disasters. To dispatch vehicles safely and minimize the misery of victims, a disaster emergency vehicle path planning model is proposed by introducing the deprivation cost function and two constraints. One constraint is the risk of the route, and the other is the angle of the vehicle. A differential evolution algorithm based on the exponential selection is developed to optimize the model. The proposed algorithm uses the exponential ranking method, making individuals have exponential probability as parents enter into the mutation stage. The approach improves the exploitation capacity while maintain the exploration ability of the algorithm. Three disaster scenarios in three-dimension are used to do simulations. The experimental results have verified that our proposed model can deal well with the vehicle path planning problem in disaster emergency management. The proposed algorithm has acquired superior performances compared with differential evolution variant algorithms.