Unmanned Aerial Vehicle-enabled grassland restoration with energy-sensitive of trajectory design and restoration areas allocation via a cooperative memetic algorithm.

Grassland restoration is a crucial method for preventing ecological degradation in grasslands. Unmanned Aerial Vehicles (UAVs) offer a promising solution to reduce extensive human labor and enhance restoration efficiency, given their fully automatic capabilities, yet their full potential remains exploited. This paper progresses this emerging technology for planning the grassland restoration. We undertake the first attempt to mathematically model the UAV-enabled restoration process as the maximization of restoration areas problem (MRAP). This model considers factors including limited UAV battery energy, grass seed weight, the number of restored areas, and their sizes. The MRAP is a composite problem involving trajectory design and area allocation, which are highly coupled and conflicting. Consequently, it requires solving two NP-hard subproblems: the variant Traveling Salesman Problem (TSP) and the Multidimensional Knapsack Problem (MKP) simultaneously. To address this complex problem, we introduce a novel cooperative memetic algorithm. The algorithm integrates an efficient heuristic algorithm, variant population-based incremental learning (PBIL), and a maximum-residual-energy-based local search (MRELS) strategy, referred to as CHAPBILM. The algorithm solves the two subproblems interlacedly by leveraging the interdependencies and inherent knowledge between them. The simulation results demonstrate that CHAPBILM successfully solves the MRAP on multiple instances in a near-optimal way. It also confirms the conflicts between trajectory design and area allocation. The effectiveness of CHAPBILM is further supported by comparisons with traditional optimization methods that do not exploit the interdependencies between the two subproblems. The proposed model and solution have the potential to be extended to other complex optimization problems in ecological protection and precision agriculture. • The maximization of restoration areas problem is first presented for the UAV-enabled grassland restoration method. • An energy-sensitive mathematical programming model is formulated for the maximization of restoration areas problem under the realistic constraints. • A novel cooperative memetic algorithm CHAPBILM is explored to effectively solve the maximization restoration areas problem, without ignoring the dependence between the two stages. • The simulation results demonstrate that CHAPBILM performs significantly better than the noncooperative optimization method for the problem, which also confirms the dependency relationship. [ABSTRACT FROM AUTHOR]