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Found 2 papers, 2 papers with code
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set.
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i. e., uniform).
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