Hybrid Jump Search and Tabu Search Metaheuristic for the Unmanned Aerial Vehicle (UAV) Routing Problem

摘要

In this research, we provide a new meta-heuristic, a jump search I tabu search hybrid, for addressing the vehicle routing problem with real-life constraints. A tour construction heuristic creates candidate solutions or jump points for the problem. A tabu search algorithm uses these jump points as starting points for a guided local search. We provide statistical analysis on the performance of our algorithm and compare it to other published algorithms. Our algorithm provides solutions within 10% of the best known solutions to benchmark problems and does so in a fraction of the time required by competing algorithms. The timeliness of the solution is vitally import to the unmanned aerial vehicle (UAV) routing problem. UAVs provide the lion's share of reconnaissance support for the US military. This reconnaissance mission requires the UAVs to visit hundreds of target areas in a rapidly changing combat environment. Air vehicle operators (AVOs) must prepare a viable mission plan for the UAVs while contending with such real-life constraints as time windows, target priorities, multiple depots, heterogeneous vehicle fleet, and pop-up threats. Our algorithm provides the AVOs with the tools to perform their mission quickly and efficiently.