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Parallel Hybrid Genetic Algorithm for Maximum Clique Problem on OpenCL

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

The maximum clique problem is to find the maximum sized clique of pairwise adjacent vertices in a given graph, which is a NP-Complete problem. In this paper, an effective parallel hybrid genetic algorithm is proposed, which consists of genetic algorithm and a local optimization strategy for solving maximum clique problem. In this algorithm, selection, crossover, mutation, fitness evaluation and replacement operators are implemented parallel on OpenCL. In addition, we have tested our algorithm by using a set of benchmark instances from the DIMACS graphs. The comparison results shows that the implementation on GPU provide better performance that CPU, even when the benchmark graphs become more large and complicate.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61472293, 61379059, 61273225, 61403287 and 31201121). Supported by the Natural Science Foundation of Hubei Province, China (No. 2015CFB335), and Youth Foundation of Wuhan University of Science and Technology (No. 2015xz017).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University of Science and Technology, Wuhan, 430081, People’s Republic of China

    Li Li, Kai Zhang, Siman Yang & Juanjuan He

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China

    Kai Zhang & Juanjuan He

Authors
  1. Li Li
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  2. Kai Zhang
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  3. Siman Yang
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  4. Juanjuan He
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Corresponding author

Correspondence to Kai Zhang .

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Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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Li, L., Zhang, K., Yang, S., He, J. (2015). Parallel Hybrid Genetic Algorithm for Maximum Clique Problem on OpenCL. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_58

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_58

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49013-6

  • Online ISBN: 978-3-662-49014-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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