Particle Swarm-based Meta-Optimising on Graphical Processing Units
Alwyn V. Husselmann, Ken A. Hawick · 2013
Optimisation (global minimisation or maximisation) of complex, unknown and non-differentiable functions is a dif- ficult problem. One solution for this class of problem is the use of meta-heuristic optimisers. This involves the system- atic movement of n-vector solutions through n-dimensional parameter space, where each dimension corresponds to a parameter in the function to be optimised. These meth- ods make very little assumptions about the problem. The most advantageous of these is that gradients are not neces- sary. 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 compo- nent. Parallel PSOs on Graphical Processing Units (GPUs) allow for much greater system sizes, and a dramatic reduc- tion in compute time. Meta-optimisation presents a further super-optimiser which is used to find appropriate algorith- mic 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.