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Parallel Symbiotic Organisms Search Algorithm

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11623))

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Abstract

Symbiotic organisms search algorithm is a population-based evolutionary optimization technique that is motivated by the simulation of social behaviour that emanates from the symbiosis relationship amongst organisms in an ecosystem. It is a popular global search swarm intelligence metaheuristic that is widely being used in conjunction with several other algorithms in different fields of study. Fascinatingly, the algorithm has also been shown to have the capability of optimizing several NP-hard problems in both continuous and binary search spaces. More so, because most of the modern day real-world computational problems requires machines with high processing power and improved optimization techniques, it is important to find ways to improve the speedup of the optimization process of this algorithm, as the complexity of the problems increase. Therefore, this paper explores the possibility of improving the optimization speedup and performance of the symbiotic organisms search algorithm through parallelization methods. The proposed parallelization procedure is implemented using OpenMP on a shared memory architecture and evaluated on a set of twenty mathematical test problems. The computational results of the parallel symbiotic organisms search algorithm was compared to its serial counterpart using a measure of run-time complexity.

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Authors and Affiliations

  1. School of Computer Science, University of KwaZulu-Natal, King Edward Road, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa

    Absalom E. Ezugwu, Rosanne Els, Jean V. Fonou-Dombeu, Duane Naidoo & Kimone Pillay

Authors
  1. Absalom E. Ezugwu
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  2. Rosanne Els
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  3. Jean V. Fonou-Dombeu
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  4. Duane Naidoo
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  5. Kimone Pillay
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Corresponding author

Correspondence to Absalom E. Ezugwu .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg , Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Ezugwu, A.E., Els, R., Fonou-Dombeu, J.V., Naidoo, D., Pillay, K. (2019). Parallel Symbiotic Organisms Search Algorithm. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_52

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  • DOI: https://doi.org/10.1007/978-3-030-24308-1_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24307-4

  • Online ISBN: 978-3-030-24308-1

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