2007Unpublished venueRequires access

A Study on the G-Node and Disconnected Edges to Improve the Global and Local Locating Heuristic for GOSST Problem

In-Bum Kim, Chae-Kak Kim

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Abstract

This paper is on the enhancement of our heuristics for GOSST problem that could apply to the design of communication networks offering graduated services. This problem hewn as one of NP-Hard problems finds a network topology meeting the G-Condition with minimum construction cost. In our prior research, we proposed two heuristics. We suggest methods of selecting G-Node and disconnections for Global or Local locating heuristic in this research. The ameliorated Local locating heuristic retrenches 17% more network construction cost saving ratio and the reformed Global locating heuristic does 14% more than our primitives.

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What this paper is about

This paper is on the enhancement of our heuristics for GOSST problem that could apply to the design of communication networks offering graduated services. This problem hewn as one of NP-Hard problems finds a network topology meeting the G-Condition with minimum construction cost. In our prior research, we proposed two heuristics. We suggest methods of selecting G-Node and disconnections for Global or Local locating heuristic in this research. The ameliorated Local locating heuristic retrenches 17% more network construction cost saving ratio and the reformed Global locating heuristic does 14% more than our primitives.

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Available abstract

This paper is on the enhancement of our heuristics for GOSST problem that could apply to the design of communication networks offering graduated services. This problem hewn as one of NP-Hard problems finds a network topology meeting the G-Condition with minimum construction cost. In our prior research, we proposed two heuristics. We suggest methods of selecting G-Node and disconnections for Global or Local locating heuristic in this research. The ameliorated Local locating heuristic retrenches 17% more network construction cost saving ratio and the reformed Global locating heuristic does 14% more than our primitives.

Key concepts: Heuristics, Heuristic, Node (physics), Mathematical optimization, Computer science, Topology (electrical circuits), Distributed computing, Mathematics

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