WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems

In three linesWeCon is a neural solver for Multi-Objective Combinatorial Optimization Problems (MOCOPs). It introduces Gated Residual Fusion blocks to better integrate weights and features, a Residual Fusion block in the decoder, and an Efficient Preference Optimization method. On 4 MOCOP variants, WeCon matches POCCO-W's HyperVolume while reducing inference time by 40%.

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