21 citations · 84 across the 28 of their papers we have counts for
12 papers · 1 filter
URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization
Changliang Zhou, Canhong Yu, Shunyu Yao +4
Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a single model. However, existing n…
Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies
Qinglong Hu, Xialiang Tong, Mingxuan Yuan +3
Deep reinforcement learning has achieved impressive success in control tasks. However, its policies, represented as opaque neural networks, are often difficult for humans to unders…
LRM-1B: Towards Large Routing Model
Han Li, Fei Liu, Zhenkun Wang +1
Vehicle routing problems (VRPs) are central to combinatorial optimization with significant practical implications. Recent advancements in neural combinatorial optimization (NCO) ha…
Fine-tuning Large Language Model for Automated Algorithm Design
Fei Liu, Rui Zhang, Xi Lin +2
The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLMs within search routines to iterativel…
Learning to Insert for Constructive Neural Vehicle Routing Solver
Fu Luo, Xi Lin, Mengyuan Zhong +4
Neural Combinatorial Optimisation (NCO) is a promising learning-based approach for solving Vehicle Routing Problems (VRPs) without extensive manual design. While existing construct…
Destroy and Repair Using Hyper Graphs for Routing
Ke Li, Fei Liu, Zhengkun Wang +1
Recent advancements in Neural Combinatorial Optimization (NCO) have shown promise in solving routing problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Rout…
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
Ping Guo, Cheng Gong, Xi Lin +4
Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-different…
A Systematic Survey on Large Language Models for Algorithm Design
Fei Liu, Yiming Yao, Ping Guo +9
Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation with…
Prompt Learning for Generalized Vehicle Routing
Fei Liu, Xi Lin, Weiduo Liao +4
Neural combinatorial optimization (NCO) is a promising learning-based approach to solving various vehicle routing problems without much manual algorithm design. However, the curren…
Self-Improved Learning for Scalable Neural Combinatorial Optimization
Fu Luo, Xi Lin, Zhenkun Wang +3
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.…
Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization
Xi Lin, Yilu Liu, Xiaoyuan Zhang +3
Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods…
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Fu Luo, Xi Lin, Fei Liu +2
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by e…