The Journal of Supercomputing

, Volume 63, Issue 3, pp 757–772 | Cite as

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

  • Peter Korošec
  • Marian Vajteršic
  • Jurij Šilc
  • Rade Kutil


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.


Numerical optimization Differential ant-stigmergy algorithm Parallelization Multi-core processor 


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Peter Korošec
    • 1
  • Marian Vajteršic
    • 2
  • Jurij Šilc
    • 1
  • Rade Kutil
    • 2
  1. 1.Computer Systems DepartmentJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Department of Scientific ComputingUniversity of SalzburgSalzburgAustria

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