A randomized Delaunay triangulation heuristic for the Euclidean Steiner tree problem in ℜd
Abstract We present a heuristic for the Euclidean Steiner tree problem in ℜd for d≥2. The algorithm utilizes the Delaunay triangulation to generate candidate Steiner points for insertion, the minimum spanning tree to identify the Steiner points to remove, and second-order cone programming to optimiz...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Van Laarhoven, Jon W. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2010 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of heuristics - Springer US, 1995, 17(2010), 4 vom: 09. Juli, Seite 353-372 |
|---|---|
| Übergeordnetes Werk: |
volume:17 ; year:2010 ; number:4 ; day:09 ; month:07 ; pages:353-372 |
| Links: |
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| DOI / URN: |
10.1007/s10732-010-9137-z |
|---|
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OLC2039387587 |
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| 520 | |a Abstract We present a heuristic for the Euclidean Steiner tree problem in ℜd for d≥2. The algorithm utilizes the Delaunay triangulation to generate candidate Steiner points for insertion, the minimum spanning tree to identify the Steiner points to remove, and second-order cone programming to optimize the location of the remaining Steiner points. Unlike other ESTP heuristics relying upon Delaunay triangulation, we insert Steiner points probabilistically into Delaunay triangles to achieve different subtrees on subsets of terminal points. We govern this neighbor generation procedure with a local search framework that extends effectively into higher dimensions. We present computational results on benchmark test problems in ℜd for 2≤d≤5. | ||
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A randomized Delaunay triangulation heuristic for the Euclidean Steiner tree problem in ℜd |
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Abstract We present a heuristic for the Euclidean Steiner tree problem in ℜd for d≥2. The algorithm utilizes the Delaunay triangulation to generate candidate Steiner points for insertion, the minimum spanning tree to identify the Steiner points to remove, and second-order cone programming to optimize the location of the remaining Steiner points. Unlike other ESTP heuristics relying upon Delaunay triangulation, we insert Steiner points probabilistically into Delaunay triangles to achieve different subtrees on subsets of terminal points. We govern this neighbor generation procedure with a local search framework that extends effectively into higher dimensions. We present computational results on benchmark test problems in ℜd for 2≤d≤5. © Springer Science+Business Media, LLC 2010 |
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Abstract We present a heuristic for the Euclidean Steiner tree problem in ℜd for d≥2. The algorithm utilizes the Delaunay triangulation to generate candidate Steiner points for insertion, the minimum spanning tree to identify the Steiner points to remove, and second-order cone programming to optimize the location of the remaining Steiner points. Unlike other ESTP heuristics relying upon Delaunay triangulation, we insert Steiner points probabilistically into Delaunay triangles to achieve different subtrees on subsets of terminal points. We govern this neighbor generation procedure with a local search framework that extends effectively into higher dimensions. We present computational results on benchmark test problems in ℜd for 2≤d≤5. © Springer Science+Business Media, LLC 2010 |
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Abstract We present a heuristic for the Euclidean Steiner tree problem in ℜd for d≥2. The algorithm utilizes the Delaunay triangulation to generate candidate Steiner points for insertion, the minimum spanning tree to identify the Steiner points to remove, and second-order cone programming to optimize the location of the remaining Steiner points. Unlike other ESTP heuristics relying upon Delaunay triangulation, we insert Steiner points probabilistically into Delaunay triangles to achieve different subtrees on subsets of terminal points. We govern this neighbor generation procedure with a local search framework that extends effectively into higher dimensions. We present computational results on benchmark test problems in ℜd for 2≤d≤5. © Springer Science+Business Media, LLC 2010 |
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The algorithm utilizes the Delaunay triangulation to generate candidate Steiner points for insertion, the minimum spanning tree to identify the Steiner points to remove, and second-order cone programming to optimize the location of the remaining Steiner points. Unlike other ESTP heuristics relying upon Delaunay triangulation, we insert Steiner points probabilistically into Delaunay triangles to achieve different subtrees on subsets of terminal points. We govern this neighbor generation procedure with a local search framework that extends effectively into higher dimensions. 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