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

Jiao，Dongbin1,2; Wang，Lingyu1; Yang，Peng2,3; Yang，Weibo4; Peng，Yu1; Shang，Zhanhuan5; Ren，Fengyuan1

2024-07-01

10.1016/j.engappai.2024.108084

Engineering Applications of Artificial Intelligence   影响因子和分区

0952-1976

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.

Cooperative memetic algorithm Decomposition Grassland restoration Restoration area Trajectory design Unmanned Aerial Vehicle (UAV)

SCI ; EI

英语

通讯

20240815581603

Antennas ; Combinatorial optimization ; Ecology ; Heuristic algorithms ; Knowledge management ; Trajectories ; Traveling salesman problem ; Unmanned aerial vehicles (UAV)

Ecology and Ecosystems:454.3 ; Aircraft, General:652.1 ; Computer Programming:723.1 ; Computer Applications:723.5 ; Information Retrieval and Use:903.3 ; Operations Research:912.3 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5

ENGINEERING

2-s2.0-85185195378

Scopus

1.School of Information Science and Engineering,Lanzhou University,Lanzhou,730000,China
2.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Department of Statistics and Data Science,Southern University of Science and Technology,Shenzhen,518055,China
4.School of Automobile,Chang'an University,Xi'an,710064,China
5.State Key Laboratory of Grassland Agro-Ecosystem,College of Ecology,Lanzhou University,Lanzhou,730000,China

Jiao，Dongbin,Wang，Lingyu,Yang，Peng,et al. Unmanned Aerial Vehicle-enabled grassland restoration with energy-sensitive of trajectory design and restoration areas allocation via a cooperative memetic algorithm[J]. Engineering Applications of Artificial Intelligence,2024,133.

Jiao，Dongbin.,Wang，Lingyu.,Yang，Peng.,Yang，Weibo.,Peng，Yu.,...&Ren，Fengyuan.(2024).Unmanned Aerial Vehicle-enabled grassland restoration with energy-sensitive of trajectory design and restoration areas allocation via a cooperative memetic algorithm.Engineering Applications of Artificial Intelligence,133.

Jiao，Dongbin,et al."Unmanned Aerial Vehicle-enabled grassland restoration with energy-sensitive of trajectory design and restoration areas allocation via a cooperative memetic algorithm".Engineering Applications of Artificial Intelligence 133(2024).