Alwyn V. Husselmann and Ken A. Hawick
Swarm, Optimization, Particles, object oriented modeling
Optimisation (global minimisation or maximisation) of complex, unknown and non-differentiable functions is a difficult problem. One solution for this class of problem is the use of meta-heuristic optimisers. This involves the systematic movement of $n$-vector solutions through $n$-dimensional parameter space, where each dimension corresponds to a parameter in the function to be optimised. These methods make very little assumptions about the problem. The most advantageous of these is that gradients are not necessary. Population-based methods such as the Particle Swarm Optimiser (PSO) are very effective at solving problems in this domain, as they employ spatial exploration and local solution exploitation in tandem with a stochastic component. Parallel PSOs on Graphical Processing Units (GPUs) allow for much greater system sizes, and a dramatic reduction in compute time. Meta-optimisation presents a further super-optimiser which is used to find appropriate algorithmic parameters for the PSO, however, this practice is often overlooked due to its immense computational expense. We present and discuss a PSO with an overlaid super-optimiser also based on the PSO itself.
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