7 papers
WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems
Xuan Wu, Jinbiao Chen, Yang Li +7
Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize a MOCOP into multiple subprobl…
Efficient Neural Combinatorial Optimization Solver for the Min-max Heterogeneous Capacitated Vehicle Routing Problem
Xuan Wu, Di Wang, Chunguo Wu +5
Numerous Neural Combinatorial Optimization (NCO) solvers have been proposed to address Vehicle Routing Problems (VRPs). However, most of these solvers focus exclusively on single-v…
iPEAR: Iterative Pyramid Estimation with Attention and Residuals for Deformable Medical Image Registration
Heming Wu, Di Wang, Tai Ma +6
Existing pyramid registration networks may accumulate anatomical misalignments and lack an effective mechanism to dynamically determine the number of optimization iterations under…
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models
Xuan Wu, Di Wang, Chunguo Wu +4
Recent studies exploited Large Language Models (LLMs) to autonomously generate heuristics for solving Combinatorial Optimization Problems (COPs), by prompting LLMs to first provide…
GELD: A Unified Neural Model for Efficiently Solving Traveling Salesman Problems Across Different Scales
Yubin Xiao, Di Wang, Rui Cao +3
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications. Recent advancements in neural network-based TSP solvers…
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Xuan Wu, Di Wang, Lijie Wen +6
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs) have been conducted, they did not cover t…