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

Researchers propose WeCon, an efficient Weight-Conditioned neural solver for Multi-Objective Combinatorial Optimization Problems (MOCOPs). It improves weight-conditioned context modeling and preference optimization, addressing limitations of existing methods in weight injection and constructing informative solution pairs for training.