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A Graph Pointer Network-Based Multi-Objective Deep Reinforcement Learning Algorithm for Solving the Traveling Salesman Problem

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Advanced Distributed Systems
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Multi-objective Solution of Traveling Salesman Problem with Time
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Machine Learning in Radiation Oncology
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Advances in Multi-Objective Nature Inspired Computing
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MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization
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Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure
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Multi-objective deep reinforcement learning for emergency scheduling in a water distribution network
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Ant-Q: A Reinforcement Learning approach to the traveling salesman problem
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Graph neural networks: A review of methods and applications
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On the influence of the number of algorithms, problems, and independent runs in the comparison of evolutionary algorithms
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The impact of Quality Indicators on the rating of Multi-objective Evolutionary Algorithms
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Maximum number of generations as a stopping criterion considered harmful
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A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy
inferredcites →https://doi.org/10.1016/j.ejor.2020.11.016openalex-snapshot
Performance indicators in multiobjective optimization
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A multi-objective deep reinforcement learning framework
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A chess rating system for evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms
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Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
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A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
inferredcites →https://doi.org/10.1088/1742-6596/887/1/012065openalex-snapshot
A hybrid genetic algorithm for solving bi-objective traveling salesman problems
inferredcites →https://doi.org/10.1109/4235.797969openalex-snapshot
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/access.2021.3060323openalex-snapshot
Deep Reinforcement Learning for Trustworthy and Time-Varying Connection Scheduling in a Coupled UAV-Based Femtocaching Architecture
inferredcites →https://doi.org/10.1109/access.2022.3181164openalex-snapshot
Multiobjective Deep Reinforcement Learning for Recommendation Systems
inferredcites →https://doi.org/10.1109/cec.2006.1688406openalex-snapshot
Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion
inferredcites →https://doi.org/10.1109/cec.2017.7969481openalex-snapshot
Ranking Multi-Objective Evolutionary Algorithms using a chess rating system with Quality Indicator ensemble
inferredcites →https://doi.org/10.1109/globecom46510.2021.9685354openalex-snapshot
Multi-objective Mobile Charging Scheduling on the Internet of Electric Vehicles: a DRL Approach
inferredcites →https://doi.org/10.1109/icassp.1998.674402openalex-snapshot
Using Markov decision process for learning dialogue strategies
inferredcites →https://doi.org/10.1109/iccecome.2018.8659266openalex-snapshot
Applying Deep Learning and Reinforcement Learning to Traveling Salesman Problem
inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9534083openalex-snapshot
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization
inferredcites →https://doi.org/10.1109/mci.2017.2742868openalex-snapshot
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Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems [Research Frontier]
inferredcites →https://doi.org/10.1109/msp.2017.2743240openalex-snapshot
Deep Reinforcement Learning: A Brief Survey
inferredcites →https://doi.org/10.1109/tcyb.2020.2977661openalex-snapshot
Deep Reinforcement Learning for Multiobjective Optimization
inferredcites →https://doi.org/10.1109/tevc.2005.851275openalex-snapshot
A faster algorithm for calculating hypervolume
inferredcites →https://doi.org/10.1109/tnsm.2021.3127685openalex-snapshot
Multi-Objective Deep Reinforcement Learning Assisted Service Function Chains Placement
inferredcites →https://doi.org/10.1109/tsmcc.2012.2218595openalex-snapshot
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
inferredcites →https://doi.org/10.1201/9781351006620-6openalex-snapshot
Deep Reinforcement Learning
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Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice
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TSPLIB—A Traveling Salesman Problem Library
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A JSSP solution for production planning optimization combining industrial engineering and evolutionary algorithms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Decomposed Multi-objective Method Based on Q-Learning for Solving Multi-objective Combinatorial Optimization Problem
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A deep reinforcement learning algorithm framework for solving multi-objective traveling salesman problem based on feature transformation
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107904openalex-snapshot
Optimizing the multi-objective traveling salesman problem with a deep reinforcement learning algorithm using cross fusion attention networks
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Time-reliability optimization for the stochastic traveling salesman problem
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Deep reinforcement learning for multi-objective combinatorial optimization: A case study on multi-objective traveling salesman problem
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Learning for routing: A guided review of recent developments and future directions
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New heuristic techniques for solving the multi-objective travelling salesman problem
inferred← citeshttps://doi.org/10.1109/access.2024.3425161openalex-snapshot
Rapid Sequence Generation for Active Debris Removal Mission Based on Attention Mechanism and Pointer Network
inferred← citeshttps://doi.org/10.1109/access.2024.3505436openalex-snapshot
Deep Reinforcement Learning Algorithm Based on Graph Weight Multi-Pointer Network for Solving Multiobjective Traveling Salesman Problem
inferred← citeshttps://doi.org/10.1109/case59546.2024.10711446openalex-snapshot
Recurrent Neural Network Based Reinforcement Learning for Inventory Control with Agent-based Supply Chain Simulator
inferred← citeshttps://doi.org/10.1109/cisp-bmei64163.2024.10906114openalex-snapshot
Deep Reinforcement Learning Using Graph Conv-LSTM for Solving Multi-Objective Combinatorial Optimization Problems
inferred← citeshttps://doi.org/10.1109/lra.2024.3389817openalex-snapshot
A Hierarchical Framework for Solving the Constrained Multiple Depot Traveling Salesman Problem
inferred← citeshttps://doi.org/10.1109/mlccim60412.2023.00072openalex-snapshot
A New Heuristic Algorithm Based on Christofides Algorithm and Nearby Measures for TSP Problem
inferred← citeshttps://doi.org/10.1109/tai.2024.3499946openalex-snapshot
Multiobjective Optimization for Traveling Salesman Problem: A Deep Reinforcement Learning Algorithm via Transfer Learning
inferred← citeshttps://doi.org/10.1109/tai.2025.3614210openalex-snapshot
Towards Generalizable Meta-Deep Reinforcement Learning Algorithm for Multiobjective Traveling Salesman Problems
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A Scalable and Adaptable Supervised Learning Approach for Solving the Traveling Salesman Problems
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LI2: A New Learning-Based Approach to Timely Monitoring of Points-of-Interest With UAV
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Combining Graph Neural Networks and Deep Reinforcement Learning for Aircraft Maintenance Stand Scheduling
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Multi-Objective Reinforcement Learning with Enhanced Pointer Networks for TSP
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QR-MO: Q-Routing for Multi-Objective Shortest-Path Computation in 5G-MEC Systems
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An Archive-Guided Equilibrium Optimizer Based on Epsilon Dominance for Multi-Objective Optimization Problems
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