9 citations · 24 across the 7 of their papers we have counts for
4 papers · 1 filter
Hierarchical Neural Constructive Solver for Real-world TSP Scenarios
Yong Liang Goh, Zhiguang Cao, Yining Ma +3
Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learnin…
Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
Jiacheng Chen, Zeyuan Ma, Hongshu Guo +3
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers. Despite their success, t…
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
Yining Ma, Zhiguang Cao, Yeow Meng Chee
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for routing problems. It learns to perform flexible k-opt exchanges based on a tailored act…
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Jinbiao Chen, Zizhen Zhang, Zhiguang Cao +4
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respe…