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Works37 from public data
- An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem109
This paper reviews recent advances in learning-based optimization (LBO) techniques and divides relevant approaches into end-to-end approaches and step-by-step approaches, and concludes the applicable types of problems for different LBO algorithms.
- DL-DRL: A Double-Level Deep Reinforcement Learning Approach for Large-Scale Task Scheduling of Multi-UAV105
A double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework (DCF), where the upper-level DRL model is responsible for the task allocation, and the lower-level DRL model is responsible for the UAV route planning to solve the practical task scheduling problem of multi-UAV in real world.
- An Autonomous Path Planning Method for Unmanned Aerial Vehicle Based on a Tangent Intersection and Target Guidance Strategy97
This article proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT), which can generate satisfactory collision-free paths under uncertain environments in a nearly real-time manner.
- Evolutionary many-Objective algorithm based on fractional dominance relation and improved objective space decomposition strategy95
A new fractional dominance relation is proposed to distinguish non-dominated solutions for strengthening convergence and the objective space decomposition approach is improved with a subspace selection mechanism to maintain the population diversity.
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- An Adaptive Reference Vector Adjustment Strategy and Improved Angle‐Penalized Value Method for RVEA8
An improved angle-penalized distance (APD) method is developed to better distinguish solutions with sound convergence performance in each subspace to improve an existing decomposition-based algorithm called reference vector-guided evolutionary algorithm (RVEA).
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- Unicorn: A Universal and Collaborative Reinforcement Learning Approach Toward Generalizable Network-Wide Traffic Signal Control5
This work presents Unicorn, a universal and collaborative MARL framework designed for efficient and adaptable network-wide ATSC, which consistently outperforms other methods across various evaluation metrics, highlighting its superiority and adaptability in optimizing traffic flows in complex, dynamic traffic networks.
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- Deep Reinforcement Learning-Based Path Planning for Uncrewed Systems: A Survey2
This review provides a comprehensive overview of the fundamentals of path planning and deep reinforcement learning (DRL), laying the foundation for understanding the potential and limitations of DRL in uncrewed system applications.
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- LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control1
A novel learning paradigm named LATS is proposed that integrates LLMs and MARL, leveraging the former's strong prior knowledge and inductive abilities to enhance the latter's decision-making process, leading to improved overall performance and generalization over both traditional RL and LLM-only approaches.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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