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A Parallel Adaptive Swarm Search Framework for Solving Black-Box Optimization Problems

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Architecture of Computing Systems – ARCS 2019 (ARCS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11479))

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Abstract

This paper presents a framework to support parallel swarm search algorithms for solving black-box optimization problems. Looking at swarm based optimization, it is important to find a well fitted set of parameters to increase the convergence rate for finding the optimum. This fitting is problem dependent and time-consuming. The presented framework automates this fitting. After finding parameters for the best algorithm, a good mapping of algorithmic properties onto a parallel hardware is crucial for the overall efficiency of a parallel implementation. Swarm based algorithms are population based, the best number of individuals per swarm and, in the parallel case, the best number of swarms in terms of efficiency and/or performance has to be found. Data dependencies result in communication patterns that have to be cheaper in terms of execution times than the computing in between communications. Taking all this into account, the presented framework enables the programmer to implement efficient and adaptive parallel swarm search algorithms. The approach is evaluated through benchmarks and real world problems.

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Notes

  1. 1.

    https://www.openmp.org/. Accessed 2 Dec 2018.

  2. 2.

    The ratio of the sequential execution time to the parallel execution time.

  3. 3.

    https://xlinux.nist.gov/dads/HTML/binarySearch.html. Accessed 2 Dec 2018.

  4. 4.

    https://www.mcs.anl.gov/research/projects/mpi/. Accessed 2 Dec 2018.

  5. 5.

    https://github.com/rshuka/PASS. Accessed 2 Dec 2018.

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Authors and Affiliations

  1. Institute of Systems Engineering, Leibniz Universität Hannover, Hannover, Germany

    Romeo Shuka & Jürgen Brehm

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  1. Romeo Shuka
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  2. Jürgen Brehm
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Correspondence to Romeo Shuka .

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Editors and Affiliations

  1. Technical University of Denmark, Lyngby, Denmark

    Martin Schoeberl

  2. Technical University of Darmstadt, Darmstadt, Germany

    Christian Hochberger

  3. Airbus Defence and Space GmbH, Taufkirchen, Germany

    Sascha Uhrig

  4. University of Hanover, Hanover, Germany

    Jürgen Brehm

  5. Otto-von-Guericke University, Magdeburg, Germany

    Thilo Pionteck

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Shuka, R., Brehm, J. (2019). A Parallel Adaptive Swarm Search Framework for Solving Black-Box Optimization Problems. In: Schoeberl, M., Hochberger, C., Uhrig, S., Brehm, J., Pionteck, T. (eds) Architecture of Computing Systems – ARCS 2019. ARCS 2019. Lecture Notes in Computer Science(), vol 11479. Springer, Cham. https://doi.org/10.1007/978-3-030-18656-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-18656-2_8

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  • Print ISBN: 978-3-030-18655-5

  • Online ISBN: 978-3-030-18656-2

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