Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
Authors: Jinbiao Chen, Jiahai Wang, Zizhen Zhang, Zhiguang Cao, Te Ye, Siyuan Chen
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |