A hierarchical parallel genetic approach for the graph coloring problem

Abbasian, Reza; Mouhoub, Malek

doi:10.1007/s10489-013-0429-5

Cited by 19 publications

(4 citation statements)

References 29 publications

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“…In the near future we will investigate advanced variables ordering heuristics [9] as weill as evolutionary techniques using a parallel architecture [25,26]. We will also intend to explore path consistency algorithms [27] to discover temporal constraints in the preprocessing phase.…”

Section: Discussionmentioning

confidence: 99%

A Multi-Phase Hybrid Metaheuristics Approach for the Exam Timetabling

Hmer

Mouhoub

2016

Int. J. Comp. Intel. Appl.

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We propose a Multi-Phase Hybrid Metaheuristics approach for solving the Exam Timetabling Problem (ETP). This approach is defined with three phases: pre-processing phase, construction phase and enhancement phase. The pre-processing phase relies on our variable ordering heuristic as well as a form of transitive closure for discovering implicit constraints. The construction phase uses a variant of the Tabu Search with conflicts dictionary. The enhancement phase includes Hill Climbing (HC), Simulated Annealing (SA) and our updated version of the extended “Great Deluge” algorithm. In order to evaluate the performance of the different phases of our proposed approach, we conducted several experiments on instances taken from ITC 2007 benchmarking datasets. The results are very promising and competitive with the well known ETP solvers.

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Section: Discussionmentioning

confidence: 99%

A Multi-Phase Hybrid Metaheuristics Approach for the Exam Timetabling

Hmer

Mouhoub

2016

Int. J. Comp. Intel. Appl.

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“…-Parallelization strategies can be combined to exploit complementary ways of parallelizations. For example, low-level and domain decomposition parallelism have been jointly applied to branch-and-X algorithms [Vu andDerbel, 2016, Adel et al, 2016] and to dynamic programming [Maleki et al, 2016], and low-level and multi-search parallelism to genetic algorithms [Abbasian andMouhoub, 2013, Munawar et al, 2009]. In total, we found eight studies which apply such combinations.…”

Section: Algorithmic Parallelization and Computational Parallelizationmentioning

confidence: 99%

“…Genetic algorithm [Massobrio et al, 2017, Liu et al, 2016, Dorronsoro et al, 2013, Defersha and Chen, 2008, Huang et al, 2012, Liu and Wang, 2015, Defersha and Chen, 2012, Homberger, 2008, Tosun et al, 2013, Zhang et al, 2016, Lu et al, 2014, Abu-lebdeh et al, 2016, Kang et al, 2016, He et al, 2010, Limmer and Fey, 2017, Abbasian and Mouhoub, 2013, Roberge et al, 2013, Lančinskas and Žilinskas, 2013, Lančinskas and Żilinskas, 2012, Lazarova and Borovska, 2008, Sancı and İşler, 2011, Umbarkar et al, 2014, Zhao et al, 2011, Vallada and Ruiz, 2009, Arellano-Verdejo et al, 2017] Other evolutionary algorithms [Fabris and Krohling, 2012, Pedroso et al, 2017, Cao et al, 2017, Dorronsoro et al, 2013, Aldinucci et al, 2016, Figueira et al, 2010, Derbel et al, 2014, Baños et al, 2014, Nebro and Durillo, 2010, Nowotniak and Kucharski, 2011, Redondo et al, 2008, Weber et al, 2011, Zhao et al, 2011, Izzo et al, 2009 Scatter search & path relinking …”

Section: Algorithm Typementioning

confidence: 99%

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Schryen

2020

European Journal of Operational Research

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Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.Keywords computing science · parallel optimization · computational optimization · literature review * I am grateful for the support provided by Abdullah Burak, Philip Empl, Constanze Hilmer, Gerhard Rauchecker, Richard Schuster, Henning Siemes, and Melih Yilmaz, who supported me substantially in searching and coding research articles.2 Impressive computational results of applying parallelization to the traveling salesman problem (TSP) are reported by Crainic et al. [2006, p.2]. arXiv:1910.03028v1 [cs.DC] 3 Oct 2019Parallel computational optimization in operations research is challenging in general from both the algorithmic and the computational perspective, and ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors. But this growth process reached a limit already several years ago due to heat dissipation and energy consumption issues . This development makes parallelization efforts (not only in optimization) much more important than it was in earlier times.Fortunately, the need for parallelization has been acknowledged and accompanied by an increased availability of parallel computing resources. This availability is rooted in two phenomena: a) the rapid development of para...

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“…Their approach do fast coloring but does not gives optimal solution. R Abbasian and M Mouhoub gave their effort to give solution using genetic algorithms approach [16].…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Performance comparison of parallel graph coloring algorithms on BSP model using hadoop

Gandhi

Misra

2015

2015 International Conference on Computing, Networking and Communications (ICNC)

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Nowadays, Hadoop is massively used to store large data generated by various sources. These data are often represented in large scale graphs to solve real world problems. To compute those data, many Bulk Synchronous Parallel(BSP) model based graph processing systems are available on top of hadoop such as Pregel and Stanford:Graph Processing System (GPS). The problem of graph coloring is to assign color to all the vertices such that no neighbor vertices have same color. The graph coloring problem has many practical application in real world data analytics. In this paper, we have compared the heuristic graph coloring algorithm with BSP model based on hadoop such as Local Maxima First, Local Minima-Maxima First, Local Largest Degree First, Local Smallest-Largest Degree First. We experimented our algorithms on real world graph dataset on our hadoop cluster. The result shows that Local Smallest-Largest Degree First algorithm perform better than other heuristic based algorithms, in term of runtime and number of color used.

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A hierarchical parallel genetic approach for the graph coloring problem

Cited by 19 publications

References 29 publications

A Multi-Phase Hybrid Metaheuristics Approach for the Exam Timetabling

A Multi-Phase Hybrid Metaheuristics Approach for the Exam Timetabling

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Performance comparison of parallel graph coloring algorithms on BSP model using hadoop

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