Vertex Weighting-Based Tabu Search for p-Center Problem
Authors: Qingyun Zhang, Zhipeng Lü, Zhouxing Su, Chumin Li, Yuan Fang, Fuda Ma
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational experiments on 138 most commonly used benchmark instances show that VWTS is highly competitive comparing to the state-of-the-art methods in spite of its simplicity. (Abstract) / In order to evaluate the effectiveness of the proposed VWTS algorithm, we conduct extensive experiments on the well-known datasets, and compare VWTS with the state-of-the-art algorithms (Section 4) |
| Researcher Affiliation | Collaboration | Qingyun Zhang1 , Zhipeng L u1 , Zhouxing Su1 , Chumin Li2 , Yuan Fang1 and Fuda Ma3 1SMART, School of Computer Science and Technology, Huazhong University of Science and Technology, China 2MIS, Universit e de Picardie Jules Verne, France 3Huawei Cloud Alkaid Lab, Huawei Technologies Co., Ltd., China |
| Pseudocode | Yes | The main framework of the proposed VWTS algorithm is presented in Algorithm 1. (Section 3) / Algorithm 1 The main framework of the VWTS algorithm / Algorithm 2 Find the best swap pair / Algorithm 3 Open a center virtually / Algorithm 4 Make a swap move |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the proposed VWTS algorithm is openly available. |
| Open Datasets | Yes | There are mainly two sets of benchmark instances for the p-center problem. The first set consists of 40 small instances (pmed) from OR-Library [Beasley, 1990]. The second set contains 98 instances from TSPLIB which are based on planar graphs and derived from real-world applications [Reinelt, 1991]. |
| Dataset Splits | No | The paper mentions benchmark instances and their use in experiments but does not provide specific details on training, validation, or test dataset splits. The problem is approached by solving a series of subproblems, rather than training a model on a split dataset. |
| Hardware Specification | Yes | All experiments are carried out on Windows Server 2012 x64 with Intel Xeon E5-2609v2 2.50 GHz CPU. |
| Software Dependencies | Yes | Our proposed VWTS algorithm is programmed in C++ and compiled with Visual Studio 2017. |
| Experiment Setup | Yes | We run a reimplemented PBS algorithm [Pullan, 2008] under a 5-millisecond time limit to obtain a good initial upper bound of the covering radius rq0 for each instance. We performed 20 independent runs on each instance, and the cutoff time for each subproblem induced by each covering radius is 6 minutes. |