Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
Authors: Jinbiao Chen, Jiahai Wang, Zizhen Zhang, Zhiguang Cao, Te Ye, Siyuan Chen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time. |
| Researcher Affiliation | Academia | Jinbiao Chen1, Jiahai Wang1,2,3, , Zizhen Zhang1, , Zhiguang Cao4, Te Ye1, Siyuan Chen1 1School of Computer Science and Engineering, Sun Yat-sen University, P.R. China 2Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, P.R. China 3Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R. China 4School of Computing and Information Systems, Singapore Management University, Singapore |
| Pseudocode | Yes | Algorithm 1 Accelerated training process |
| Open Source Code | Yes | Our code is publicly available2 (https://github.com/bill-cjb/EMNH) |
| Open Datasets | Yes | We conduct computational experiments to evaluate the proposed method on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP). Following the convention in [26, 12], we consider the instances of different sizes n=20/50/100 for MOTSP/MOCVRP and n=50/100/200 for MOKP. [...] We test the generalization ability of the model on 200 larger-scale random instances (n=150/200) and 3 commonly used MOTSP benchmark instances (Kro AB100/150/200) in TSPLIB [52]. |
| Dataset Splits | No | The paper mentions 'a validation dataset' but does not provide specific details about its split or size, such as percentages or sample counts. |
| Hardware Specification | Yes | All experiments are run on a PC with an Intel Xeon 4216 CPU and an RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The meta-learning rate ϵ is linearly annealed to 0 from ϵ0 = 1 initially. A constant learning rate of the Adam optimizer is set to 10^-4. We set B = 64, Tm = 3000, Tu = 100, and N = M. |