Parallel Parametric Optimisation with Firefly Algorithms on Graphical Processing Units
Alwyn V. Husselmann, Ken A. Hawick · 2012
Parametric optimisation techniques such as Particle Swarm Optimisation (PSO), Firefly algorithms (FAs), genetic algorithms (GAs) are at the centre of atten- tion 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 de- signing algorithms such as these is the relative ease of performance testing and evaluation. In the litera- ture, a set of well-defined functions, often with one global minimum and several local minima is avail- able 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 algo- rithm. We also discuss spatial partitioning techniques to dramatically reduce redundant entity interactions introduced by our modifications of the Firefly algo- rithm.