Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem

With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the en...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 20; p. 6698
Main Authors: Shu, Xiulan, Lin, Anping, Wen, Xupeng
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 18.10.2024
MDPI
Subjects:
Algorithms
Data mining
Deep learning
deep reinforcement learning
drone routing
Drones
Energy conservation
Energy consumption
Energy efficiency
Energy management systems
energy savings
Energy use
Genetic algorithms
Heuristic
Learning strategies
Logistics
Machine learning
Methods
multiple agents
Neural networks
Optimization
Planning
Traveling salesman problem
drone routing
energy savings
deep reinforcement learning
multiple agents
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL’s superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24206698