Complex Mission Planning for UAVs: A Multiple Objective Hybrid Neighborhood Search Approach

In this paper, we deal with the complex mission planning problem for unmanned aerial vehicles (UAVs) via a multiple objective hybrid neighborhood search approach, in which resource allocation, task assignment and flight profile selection are incorporated in a unified framework. Three objective functions, namely, the maximum completion time, the total energy consumption and the task payback are optimized. To address the coupled submissions simultaneously, we propose a Multi-Objective Hybrid Adaptive Large Neighborhood Search (MO-HALNS) algorithm which extends the conventional Adaptive Large Neighborhood Search (ALNS) algorithm to a muli-objective version by using a population-based approach. Additionally, an evolutionary mechanism is developed, thereby managing the Pareto-optimal fronts while reducing computational costs. Simulation results demonstrate the effectiveness and superiority of the proposed framework.