2 citations · 3 across the 12 of their papers we have counts for
5 papers · 1 filter
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Ke Zhao, Zixiang Di, Hong Qian +9
Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented…
A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization
Yaoming Yang, Shuai Wang, Bingdong Li +2
Dynamic multi-objective optimization requires continuous tracking of moving Pareto fronts. Existing methods struggle with irregular mutations and data sparsity, primarily facing th…
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network
Bingdong Li, Mei Jiang, Hong Qian +3
Evolutionary Reinforcement Learning (ERL), training the Reinforcement Learning (RL) policies with Evolutionary Algorithms (EAs), have demonstrated enhanced exploration capabilities…
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
Bingdong Li, Zixiang Di, Yongfan Lu +5
Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling compl…
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization
Yongfan Lu, Zixiang Di, Bingdong Li +5
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to tran…