#### Computational Science Technical Note CSTN-161

# Particle Swarm-Based Meta-Optimising on Graphical Processing Units

## A. V. Husselmann and K. A. Hawick

### Archived: 2013

**Abstract**

Optimisation (global minimisation or maximisation) of complex, unknown
and nondifferentiable functions is a difficult problem. One solution
for this class of problem is the use of meta-heuristic optimisation.
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.

**Keywords:** swarm; optimization; particles; PSO

**Full Document Text:** PDF version.

**Citation Information:** BiBTeX database for CSTN Notes.

**BiBTeX reference:**

@INPROCEEDINGS{CSTN-161,
author = {A. V. Husselmann and K. A. Hawick},
title = {Particle Swarm-Based Meta-Optimising on Graphical Processing Units},
booktitle = {Proc. Int. Conf. on Modelling, Identification and Control (AsiaMIC
2013)},
year = {2013},
pages = {104-111},
address = {Phuket, Thailand},
month = {10-12 April},
publisher = {IASTED},
note = {CSTN-161},
institution = {Computer Science, Massey University},
keywords = {swarm; optimization; particles; PSO},
owner = {kahawick},
timestamp = {2012.12.01}
}

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