A hybrid genetic algorithm for rescue path planning in uncertain adversarial environment

Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far relies on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model and a hybrid genetic algorithm are proposed to solve the rescue path planning problem for a single vehicle navigating in uncertain adversarial environment. We present a simplified mathematical linear programming formulation aimed at minimizing traveled distance and threat exposure. As an approximation to the basic problem, the user-defined model allows to specify a lower bound on the optimal solution for some particular survivability conditions. Hard problem instances are then solved using a novel hybrid genetic algorithm relaxing some of the common assumptions considered by previous path construction methods. The algorithm evolves a population of solution combining genetic operators with a new stochastic path generation technique, providing guided local search, while improving solution quality. The value of the problem-solving approach is shown for simple cases and compared to an alternate heuristic.