Neural combinatorial optimization methods based on deep reinforcement learning (DRL) have shown promising results recently in solving complex combinatorial optimization problems (COPs). However, existing approaches usually require training separate models for each COP. When facing multiobjective COPs (MOCOPs), a large...
Hangyu Lou, Xian-Peng Wang· IEEE Transactions on Neural...· 0 citations
Rolling bearings are a critical component for the reliability of rotating machinery, but traditional fault diagnosis methods rely on manually designed features, resulting in insufficient robustness under varying operating conditions. To address this issue, this paper proposes an evolutionary optimization framework that...
Adaptively adjusting the components or parameters of decomposition-based multiobjective evolutionary algorithms (MOEA/Ds) is crucial for enhancing its performance in handling multiobjective optimization problems with complex Pareto fronts. However, component design strategies and parameter tuning mechanisms based on do...
Zhi-Hua Li, Xian-Peng Wang, Zhi-Ming Dong et al.· International Conference on...· 0 citations
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