Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem
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...
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| Veröffentlicht in: | Journal of computer science and technology 2017-11, Vol.32 (6), p.1319-1334 |
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| creator | Yin, Ai-Hua Zhou, Tao-Qing Ding, Jun-Wen Zhao, Qing-Jie Lv, Zhi-Peng |
| description | 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. |
| doi_str_mv | 10.1007/s11390-017-1802-3 |
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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. 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Comput. Sci. Technol</stitle><addtitle>Journal of Computer Science and Technology</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>32</volume><issue>6</issue><spage>1319</spage><epage>1334</epage><pages>1319-1334</pages><issn>1000-9000</issn><eissn>1860-4749</eissn><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. 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| subjects | Adaptive search techniques Algorithms Apexes Artificial Intelligence Computer Science Computing time Data Structures and Information Theory Experiments Graph theory Heuristic Information Systems Applications (incl.Internet) Iterative methods Job shops Libraries Linear programming Methods Production scheduling Randomization Regular Paper Searching Software Engineering Tabu search Theory of Computation |
| title | Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem |
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