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Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem

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Abstract

The p-center problem consists of choosing a subset of vertices in an undirected graph as facilities in order to minimize the maximum distance between a client and its closest facility. This paper presents a greedy randomized adaptive search procedure with path-relinking (GRASP/PR) algorithm for the p-center problem, which combines both GRASP and path-relinking. Each iteration of GRASP/PR consists of the construction of a randomized greedy solution, followed by a tabu search procedure. The resulting solution is combined with one of the elite solutions by path-relinking, which consists in exploring trajectories that connect high-quality solutions. Experiments show that GRASP/PR is competitive with the state-of-the-art algorithms in the literature in terms of both solution quality and computational efficiency. Specifically, it virtually improves the previous best known results for 10 out of 40 large instances while matching the best known results for others.

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Authors and Affiliations

  1. School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Ai-Hua Yin

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Tao-Qing Zhou, Jun-Wen Ding, Qing-Jie Zhao & Zhi-Peng Lv

  3. Department of Computer Science, School of Information Engineering, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China

    Tao-Qing Zhou

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  1. Ai-Hua Yin
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Correspondence to Jun-Wen Ding.

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Yin, AH., Zhou, TQ., Ding, JW. et al. Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem. J. Comput. Sci. Technol. 32, 1319–1334 (2017). https://doi.org/10.1007/s11390-017-1802-3

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