Computational Science Technical Note CSTN-141

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Parallel Parametric Optimisation with Firefly Algorithms on Graphical Processing Units

A.V. Husselmann and K.A. Hawick

Archived March 2012

Abstract

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.

Keywords: optimisation; firefly algorithm; GPU; CUDA; spatial partitioning.

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@INPROCEEDINGS{CSTN-141,
  author = {Alwyn V. Husselmann and K. A. Hawick},
  title = {Parallel Parametric Optimisation with Firefly Algorithms on Graphical
	Processing Units},
  booktitle = {Proc. Int. Conf. on Genetic and Evolutionary Methods (GEM'12)},
  year = {2012},
  number = {CSTN-141},
  pages = {77-83},
  address = {Las Vegas, USA},
  month = {16-19 July},
  publisher = {CSREA},
  institution = {Computer Science, Massey University},
  timestamp = {2012.05.03}
}


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