  This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large
scale route planning and task assignment joint problem. Given a set of
constraints (e.g., time) and a set of task priority values, the goal is to find
the optimal route for underwater mission that maximizes the sum of the
priorities and minimizes the total risk percentage while meeting the given
constraints. Making use of the heuristic nature of genetic and swarm
intelligence algorithms in solving NP-hard graph problems, Particle Swarm
Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum
solution, where each individual in the population is a candidate solution
(route). To evaluate the robustness of the proposed methods, the performance of
the all PS and GA algorithms are examined and compared for a number of Monte
Carlo runs. Simulation results suggest that the routes generated by both
algorithms are feasible and reliable enough, and applicable for underwater
motion planning. However, the GA-based route planner produces superior results
comparing to the results obtained from the PSO based route planner.
