A Modified Binary Crow Search Algorithm for Solving the Graph Coloring Problem

Meraihi, Yassine; Mahseur, Mohammed; Acheli, Dalila

doi:10.4018/ijaec.2020040103

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…Meanwhile, Kusumawardani et al utilized a combination of a graph-coloring-based sequential greedy algorithm and a simulated annealing (SA) algorithm to address the Examination Timetabling Problem (ETP) using two real-world datasets [13]. Meraihi, et al presented a Chaotic Binary Salp Swarm Algorithm (CBSSA) to address GCP [14]. In a related study, Silva et al proposed a hybrid algorithm called iColourAnt, which employs ant colony optimization (ACO) and an efficient local search strategy to achieve suitable solutions for GCP.…”

Section: Literature Reviewmentioning

confidence: 99%

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2024

J Math Techniques Comput Math

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The issue of graph coloring is concerned with assigning colors to each vertex of an undirected graph so that adjacent vertices are not assigned the same color. Due to its NP-hard nature, a variety of heuristics and metaheuristics have been developed to tackle this problem. One of these approaches is the memetic algorithm, which was introduced as a solution for this problem. Another example is using a metaheuristic approach to address the round-robin sports scheduling problem. The algorithm suggested for this task comprises three primary components: (1) a randomized danger heuristic algorithm, (2) tabu search, and (3) a genetic algorithm with adaptive multi-parent crossover. To assess the performance of this algorithm, a set of test problems from different benchmark graphs in two categories were selected and compared with nine effective heuristics from existing literature. The results show that the algorithm performs exceptionally well on benchmark graphs that are known to be challenging. Additionally, a case study was conducted to demonstrate the effectiveness of the proposed algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Untitled

2024

J Math Techniques Comput Math

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“…In modeling the problem, it can be done through graphs and its implementation in graph coloring. The graph coloring issue is one of the most interesting and challenging algorithms for solving in computer science, mathematics, and operations research [9]. The problem of graph coloring can of course be connected with the field of cartography.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the greedy algorithm on graph coloring

Sipayung

Suwilo

Gultom

et al. 2022

J. Phys.: Conf. Ser.

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Graph theory is part of the field of mathematics that can be applied in various other fields of science to solve problems. One of them is the problem of determining the color on the map. The map that will be colored here is a map of the Deli Serdang regency which consists of 22 sub-districts. The coloring of the map is done by first modeling it in the form of a graph. One way to determine the minimum color of a graph is to use a greedy algorithm. From the map, we get a dual graph with 22 vertices and 41 edges. Based on the greedy algorithm that has been applied, the minimum number of colors is obtained as many as four colors, namely blue, green, red, and yellow with each district directly bordering having a different color. The results of map coloring by applying the greedy algorithm are also obtained with the help of the python 3.7 programming language.

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“…The CSA is modified to resolve the graph coloring problem and it is proposed in (Meraihi, Mahseur, & Acheli, 2020). The binary crow search algorithm is derived from the CSA and the chaotic maps are used to choose the right values for the parameters of CSA.…”

Section: Introductionmentioning

confidence: 99%

MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

Visalakshi¹,

Shanthi²,

Lakshmi³

2021

International Journal of Cognitive Informatics and Natural Intelligence

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Cluster analysis is the prominent data mining technique in knowledge discovery and it discovers the hidden patterns from the data. The K-Means, K-Modes and K-Prototypes are partition based clustering algorithms and these algorithms select the initial centroids randomly. Because of its random selection of initial centroids, these algorithms provide the local optima in solutions. To solve these issues, the strategy of Crow Search algorithm is employed with these algorithms to obtain the global optimum solution. With the advances in information technology, the size of data increased in a drastic manner from terabytes to petabytes. To make proposed algorithms suitable to handle these voluminous data, the phenomena of parallel implementation of these clustering algorithms with Hadoop Mapreduce framework. The proposed algorithms are experimented with large scale data and the results are compared in terms of cluster evaluation measures and computation time with the number of nodes.

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A Modified Binary Crow Search Algorithm for Solving the Graph Coloring Problem

Cited by 6 publications

References 37 publications

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Implementation of the greedy algorithm on graph coloring

MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data

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