3 citations · 6 across the 6 of their papers we have counts for
6 papers
Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems
Yi Chen, Zikang Yu, Jiahai Wang +3
Vehicle Routing Problems (VRPs) are fundamental combinatorial optimization problems with widespread applications in various scenarios. The advanced optimization solvers can effecti…
UCPO: A Universal Constrained Combinatorial Optimization Method via Preference Optimization
Zhanhong Fang, Debing Wang, Jinbiao Chen +2
Neural solvers have demonstrated remarkable success in combinatorial optimization, often surpassing traditional heuristics in speed, solution quality, and generalization. However,…
BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization
Zijun Liao, Jinbiao Chen, Debing Wang +2
Neural Combinatorial Optimization (NCO) has emerged as a promising approach for NP-hard problems. However, prevailing RL-based methods suffer from low sample efficiency due to spar…
A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems
Siyuan Chen, Xin Du, Jiahai Wang
Emergence, a global property of complex adaptive systems (CASs) constituted by interactive agents, is prevalent in real-world dynamic systems, e.g., network-level traffic congestio…
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
Jinbiao Chen, Jiahai Wang, Zizhen Zhang +3
Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are s…
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…