A memetic algorithm for graph coloring

Lu, Zhengding; Hao, Jin‐Kao

doi:10.1016/j.ejor.2009.07.016

Cited by 202 publications

(129 citation statements)

References 32 publications

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“…The first set ( Table 1) is composed of 29 well-known DIMACS graphs 1 . These graphs are very popular for testing graph coloring algorithms [6,11,18,20,22]. However only the 12 DSJC random graphs have been recently used for sum coloring [4,17].…”

Section: Problem Instances and Experimental Protocolmentioning

confidence: 99%

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An effective heuristic algorithm for sum coloring of graphs

Hao

2012

Computers & Operations Research

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Given an undirected graph G = (V, E), the minimum sum coloring problem (MSCP) is to find a legal vertex coloring of G, using colors represented by natural numbers (1, 2, . . .) such that the total sum of the colors assigned to the vertices is minimized. In this paper, we present EXSCOL, a heuristic algorithm based on independent set extraction for this NP-hard problem. EXSCOL identifies iteratively collections of disjoint independent sets of equal size and assign to each independent set the smallest available color. For the purpose of computing large independent sets, EXSCOL employs a tabu search based heuristic. Experimental evaluations on a collection of 52 DIMACS and COLOR2 benchmark graphs show that the proposed approach achieves highly competitive results. For more than half of the graphs used in the literature, our approach improves the current best known upper bounds.

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Section: Problem Instances and Experimental Protocolmentioning

confidence: 99%

“…For this purpose, we adopt the Memetic Coloring Algorithm (MACOL) [18] which is a recent and competitive graph coloring algorithm. For this experiment, we consider again the set of 12 DSJC random instances.…”

Section: Sum Coloring Vs Graph Coloringmentioning

confidence: 99%

An effective heuristic algorithm for sum coloring of graphs

Hao

2012

Computers & Operations Research

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“…The tabu search algorithm TabuCol (Hertz and Werra 1987) works in this solution space and uses this particular move operator. Other examples can be found in Galinier and Hao (1999), Johnson et al (1991) and Lü and Hao (2010).…”

Section: Colourings and Solution Spacesmentioning

confidence: 99%

Tackling the edge dynamic graph colouring problem with and without future adjacency information

2017

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Many real world operational research problems, such as frequency assignment and exam timetabling, can be reformulated as graph colouring problems (GCPs). Most algorithms for the GCP operate under the assumption that its constraints are fixed, allowing us to model the problem using a static graph. However, in many real-world cases this does not hold and it is more appropriate to model problems with constraints that change over time using an edge dynamic graph. Although exploring methods for colouring dynamic graphs has been identified as an area of interest with many real-world applications, to date, very little literature exists regarding such methods. In this paper we present several heuristic methods for modifying a feasible colouring at time-step t into an initial, but not necessarily feasible, colouring for a "similar" graph at time-step t + 1. We will discuss two cases; (1) where changes occur at random, and (2) where probabilistic information about future changes is provided. Experimental results are also presented and the benefits of applying these particular modification methods are investigated.

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“…Offspring I 0 is inserted into POP if it is of the best quality relative to the population, or if the minimum distance between I 0 and any other individual in the population is greater than the minimum distance between any two individuals in the population. To determine the individual that is to be replaced by I 0 , the authors adopt a strategy proposed in [33] that uses the following quality-and-distance scoring function H to rank the individuals of the population:…”

Section: The Memetic Algorithm By Benlic and Haomentioning

confidence: 99%

Hybrid Metaheuristics for the Graph Partitioning Problem

Benlic

Hao

2013

Hybrid Metaheuristics

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The Graph Partitioning Problem (GPP) is one of the most studied NPcomplete problems notable for its broad spectrum of applicability such as in VLSI design, data mining, image segmentation, etc. Due to its high computational complexity, a large number of approximate approaches have been reported in the literature. Hybrid algorithms that are based on adaptations of popular metaheuristic techniques have shown to provide outstanding performance in terms of partition quality. In particular, it is the hybrids between well-known metaheuristics and multilevel strategies that report partitions of the minimal cut-size value. However, metaheuristic hybrids generally require more computing time than those based on greedy heuristics which can generate partitions of acceptable quality in a matter of seconds even for very large graphs. This chapter is dedicated to a review on some representative hybrid metaheuristic approaches including genetic local search, basic multilevel search and recent development on hybrid multilevel search.

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A memetic algorithm for graph coloring

Cited by 202 publications

References 32 publications

An effective heuristic algorithm for sum coloring of graphs

An effective heuristic algorithm for sum coloring of graphs

Tackling the edge dynamic graph colouring problem with and without future adjacency information

Hybrid Metaheuristics for the Graph Partitioning Problem

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