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Parallel Parametric Optimisation with Firefly Algorithms on Graphical Processing Units

A.V. Husselmann, K.A. Hawick
Computer Science, Institute for Information and Mathematical Sciences, Massey University, North Shore 102-904, Auckland, New Zealand
CSTN Computational Science Technical Note Series, CSTN-141, 2012

@article{husselmann2012parallel,

   title={Parallel Parametric Optimisation with Firefly Algorithms on Graphical Processing Units},

   author={Husselmann, AV and Hawick, KA},

   year={2012}

}

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Parametric optimisation techniques such as Particle Swarm Optimisation (PSO), Firefly algorithms (FAs), genetic algorithms (GAs) are at the centre of attention in a range of optimisation problems where local minima plague the parameter space. Variants of these algorithms deal with the problems presented by local minima in a variety of ways. A salient feature in designing algorithms such as these is the relative ease of performance testing and evaluation. In the literature, a set of well-defined functions, often with one global minimum and several local minima is available to evaluate the convergence of an algorithm. This allows for simultaneously evaluating performance as well as the quality of the solutions calculated. We report on a parallel graphical processing unit (GPU) implementation of a modified Firefly algorithm, and the associated performance and quality of this algorithm. We also discuss spatial partitioning techniques to dramatically reduce redundant entity interactions introduced by our modifications of the Firefly algorithm.
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