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 [1].
A Weighted-Based Fast Local Search for α-Neighbor p-Center Problem
Authors: Qingyun Zhang, Zhipeng Lü, Junwen Ding, Zhouxing Su
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational experiments on 154 widely used public benchmark instances demonstrate that WFLS outperforms the state-of-the-art methods in the literature. Specifically, WFLS improves 69 previous best known results and matches the best know results for all the remaining ones in less time than other competitors. |
| Researcher Affiliation | Academia | Qingyun Zhang , Zhipeng L u , Junwen Ding and Zhouxing Su School of Computer Science and Technology, Huazhong University of Science and Technology, China EMAIL |
| Pseudocode | Yes | Algorithm 1 The main framework of the WFLS algorithmInput A graph G, the center number p, the value of α Output The best solution found so far X |
| Open Source Code | No | The paper provides a link to a GitHub repository stating 'The complete results are available in https://github.com/Zhangqingyun/alphaPCP-WFLS.' However, it explicitly states that the link is for 'complete results' and does not unambiguously state that the source code for the described methodology is also provided. |
| Open Datasets | Yes | There are 154 instances with α = 2, 3 for the α-p CP generated from TSP-Library [Reinelt, 1991]. |
| Dataset Splits | No | The paper uses 154 public benchmark instances from TSP-Library for an optimization problem. It does not provide specific training/test/validation dataset splits, as these are typically not applicable in the same way as for machine learning datasets. |
| Hardware Specification | Yes | Our WFLS is coded in C++ and all our experiments are carried out on Windows Server 2019 x64 with an Intel Xeon Gold 6133 2.50GHz CPU. |
| Software Dependencies | Yes | Our WFLS is coded in C++ and we find the optimal objective value for some instances using the Gurobi 11.0.3 solver with 1800 seconds time limit per instance |
| Experiment Setup | Yes | For each instance, we carried out 20 independent runs under 180 seconds time limit. The computational platform for the reference algorithms 1HSVL and 2HVSL is an Intel Xeon E5-2670v2 machine with 2.5GHz and 6GB of RAM, and the time limit for each instance is 1800 seconds. The reference algorithms MA and SO are tested on an Apple M2 Pro 12-core 3480 MHz computer with 16GB RAM running Mac OS Ventura and the time limit is 1800 seconds. Notably, there are no parameters that need to be tuned in our WFLS. |