Contemporary Methods for Graph Coloring as an Example of Discrete Optimization

The following paper provides an insight into application of the contemporary heuristic methods to graph coloring problem. Variety of algorithmic solutions for the Graph Coloring Problem (GCP) are discussed and recommendations for their implementation provided. The GCP is the NP-hard problem, aiming...

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Main Author: Adrian Bilski
Format: Article
Language:English
Published: Polish Academy of Sciences 2019-06-01
Series:International Journal of Electronics and Telecommunications
Subjects:
Online Access:https://journals.pan.pl/Content/110219/PDF/32.pdf
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author Adrian Bilski
author_facet Adrian Bilski
author_sort Adrian Bilski
collection DOAJ
description The following paper provides an insight into application of the contemporary heuristic methods to graph coloring problem. Variety of algorithmic solutions for the Graph Coloring Problem (GCP) are discussed and recommendations for their implementation provided. The GCP is the NP-hard problem, aiming at finding the minimum number of colors for vertices in such a way that none of two adjacent vertices are marked with the same color. With the advent of modern processing units metaheuristic approaches to solve GCP were extended to discrete optimization here. To explain the phenomenon of these methods, a thorough survey of AI-based algorithms for GCP is provided, with the main differences between specific techniques pointed out.
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spelling doaj.art-1ddaf2af9a1d4359b1b0c96603b657282022-12-22T01:40:34ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332019-06-01vol. 65No 2235243https://doi.org/10.24425/ijet.2019.126306Contemporary Methods for Graph Coloring as an Example of Discrete OptimizationAdrian BilskiThe following paper provides an insight into application of the contemporary heuristic methods to graph coloring problem. Variety of algorithmic solutions for the Graph Coloring Problem (GCP) are discussed and recommendations for their implementation provided. The GCP is the NP-hard problem, aiming at finding the minimum number of colors for vertices in such a way that none of two adjacent vertices are marked with the same color. With the advent of modern processing units metaheuristic approaches to solve GCP were extended to discrete optimization here. To explain the phenomenon of these methods, a thorough survey of AI-based algorithms for GCP is provided, with the main differences between specific techniques pointed out.https://journals.pan.pl/Content/110219/PDF/32.pdfgraph coloringchromatic numbermetaheuristics
spellingShingle Adrian Bilski
Contemporary Methods for Graph Coloring as an Example of Discrete Optimization
International Journal of Electronics and Telecommunications
graph coloring
chromatic number
metaheuristics
title Contemporary Methods for Graph Coloring as an Example of Discrete Optimization
title_full Contemporary Methods for Graph Coloring as an Example of Discrete Optimization
title_fullStr Contemporary Methods for Graph Coloring as an Example of Discrete Optimization
title_full_unstemmed Contemporary Methods for Graph Coloring as an Example of Discrete Optimization
title_short Contemporary Methods for Graph Coloring as an Example of Discrete Optimization
title_sort contemporary methods for graph coloring as an example of discrete optimization
topic graph coloring
chromatic number
metaheuristics
url https://journals.pan.pl/Content/110219/PDF/32.pdf
work_keys_str_mv AT adrianbilski contemporarymethodsforgraphcoloringasanexampleofdiscreteoptimization