Papers › Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

22 Oct 2023NeurIPS 2023 11arXiv:2310.15195archive 2025-07-28

Jinbiao Chen, Zizhen Zhang, Zhiguang Cao, Yaoxin Wu, Yining Ma, Te Ye, Jiahai Wang

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. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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multi_head_attention bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository ran MIT (permissive) · 63b6c1a824b8f874 · report
ACTOR bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository unverified MIT (permissive) · 4b869d32d880cb2c · report
Add_And_Normalization_Module bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository unverified MIT (permissive) · 0730cbbd705e0936 · report
Encoder bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository unverified MIT (permissive) · d7da849db6e8b220 · report
Encoder_Layer bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository unverified MIT (permissive) · 54497d0a2a7895b3 · report
Feed_Forward_Module bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository unverified MIT (permissive) · 1796f9e6cb144005 · report
Next_Node_Probability_Calculator_for_group bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository unverified MIT (permissive) · f664ca98a835ad32 · report
pick_nodes_for_each_group bill-cjb/nhde/NHDE-M/TSP/source/MODEL__Actor/grouped_actors.py official repository unverified MIT (permissive) · be383062e9d9f618 · report

Tasks

Combinatorial OptimizationDeep Reinforcement LearningDiversityGraph Attention

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