nhde-m
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement (Appendix) A Reference point and hypervolume ratio
In the inference process, the submodel is used to solve the corresponding subproblem. The input dimensions of the node features vary with different problems. A masking mechanism is adopted in each decoding step to ensure the solution feasibility. For MOTSP, the visited nodes are masked. NHDE-M usually spends relatively more inference time than MDRL with the same number of weights.
- Europe > Netherlands > North Brabant > Eindhoven (0.04)
- Asia > Singapore > Central Region > Singapore (0.04)
- Asia > China > Guangdong Province > Guangzhou (0.04)
- Europe > Netherlands > North Brabant > Eindhoven (0.04)
- Asia > Singapore > Central Region > Singapore (0.04)
- Asia > China > Guangdong Province > Guangzhou (0.04)
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement (Appendix) A Reference point and hypervolume ratio
In the inference process, the submodel is used to solve the corresponding subproblem. The input dimensions of the node features vary with different problems. A masking mechanism is adopted in each decoding step to ensure the solution feasibility. For MOTSP, the visited nodes are masked. NHDE-M usually spends relatively more inference time than MDRL with the same number of weights.