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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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- CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem–
CAMO is proposed, a conditional neural solver for MOMTSP that generalizes across varying numbers of targets, agents, and preference vectors, and yields high-quality approximations to the Pareto front (PF).
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- Learning to Solve Compositional Geometry Routing Problems–
DiCon, a differential attention-assisted solver with contrastive learning, is proposed as a plug-and-play framework that tackles the CGRP problem from two complementary angles with strong performance, broad versatility, and superior generalization across diverse CGRP instances with different compositions.
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- CROSS: A Mixture-of-Experts Reinforcement Learning Framework for Generalizable Large-Scale Traffic Signal Control–
CROSS, a novel Mixture-of-Experts (MoE)-based decentralized RL framework for generalizable ATSC, is proposed that achieves superior performance and generalization through improved representation of diverse traffic scenarios.
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- Digital-Intelligence Empowerment in the Multi-Dimensional Communication and Inheritance of Chinese and Foreign Literary Classics–
Solutions for addressing the challenges of transmitting literary classics across cultures are proposed, aiming to provide global literary enthusiasts with more accessible, efficient, and profound literary experiences while fostering cross-cultural exchange and intergenerational heritage of literary classics.
- A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem–
Experimental results show that UD3RL outperforms conventional methods in both solution quality and runtime, while exhibiting strong generalization across problem scales, spatial distributions, and radius ranges, as well as robustness to dynamic environments.
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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