AI RESEARCH
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation
arXiv CS.AI
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ArXi:2506.08898v3 Announce Type: replace Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multiple subproblems, each associated with a specific weight vector. However, these methods typically treat all subproblems equally and solve them using a single model, hindering the effective exploration of the solution space and thus leading to suboptimal performance.