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Multi-core implementation of the differential ant-stigmergy algorithm for numerical optimization

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Abstract

Numerical optimization techniques are applied to a variety of engineering problems. The cost-function evaluation is an important part of any numerical optimization and is usually realized as a black-box simulator. For the efficient solving of the numerical optimization problem on multi-core systems, new shared-memory and distributed-memory approaches are proposed. The algorithms are based on an ant-stigmergy meta-heuristics, where indirect coordination between the ants drives the search procedure toward the optimal solution. Indirect coordination offers a high degree of parallelism and therefore relatively straightforward shared-memory and distributed-memory implementations. The Intel-OpenMP 3.0 and MPICH2 libraries are used for the inter-thread and inter-process communications, respectively. It is shown that speed-up strongly depends on the simulation time. This is especially evident in a distributed-memory implementation. Therefore, the algorithms’ performances, according to the simulator’s time complexity, are experimentally evaluated and discussed.

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Authors and Affiliations

  1. Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec & Jurij Šilc

  2. Department of Scientific Computing, University of Salzburg, Salzburg, Austria

    Marian Vajteršic & Rade Kutil

Authors
  1. Peter Korošec
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  2. Marian Vajteršic
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  3. Jurij Šilc
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  4. Rade Kutil
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Correspondence to Peter Korošec.

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Korošec, P., Vajteršic, M., Šilc, J. et al. Multi-core implementation of the differential ant-stigmergy algorithm for numerical optimization. J Supercomput 63, 757–772 (2013). https://doi.org/10.1007/s11227-012-0772-z

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