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A Parallel Hybrid Genetic Algorithm for Solving the Maximum Clique Problem

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High-Performance Computing and Big Data Analysis (TopHPC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 891))

Abstract

Finding maximum complete subgraph (maximum clique) from the input graph is an NP-Complete problem. When the graph size grows, the genetic algorithm is an appropriate candidate for solving this problem. However, due to the reduced computational complexity, the genetic algorithm can solve the problem, but it can still be time consuming to solve big problems. To tackle this weakness, we parallelize our hybrid genetic algorithm for solving the maximum clique problem. In this direction, we have parallelized producing, repairing and evaluation of chromosomes. Experimental results by using a set of benchmark instances from the DIMACS graphs indicate that the proposed meta heuristic algorithm in almost all cases, it finds optimal or near optimal answer. Also, the efficiency of the proposed parallelization is more than 3.44 times faster on an 8-core processor in comparison to the sequential implementation.

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Authors and Affiliations

  1. Department of Data and Computer Science, Faculty of Mathematical Sciences, GC Shahid Beheshti University, Tehran, Iran

    Mohammad Kazem Fallah, Vahid Salehi Keshvari & Mahmood Fazlali

Authors
  1. Mohammad Kazem Fallah
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  2. Vahid Salehi Keshvari
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  3. Mahmood Fazlali
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Corresponding author

Correspondence to Mahmood Fazlali .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Lucio Grandinetti

  2. Kharazmi University, Tehran, Iran

    Seyedeh Leili Mirtaheri

  3. University of Calabria, Rende, Italy

    Reza Shahbazian

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Fallah, M.K., Keshvari, V.S., Fazlali, M. (2019). A Parallel Hybrid Genetic Algorithm for Solving the Maximum Clique Problem. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-33495-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33494-9

  • Online ISBN: 978-3-030-33495-6

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