Computational Science Technical Note CSTN-161


Particle Swarm-Based Meta-Optimising on Graphical Processing Units

A. V. Husselmann and K. A. Hawick

Archived: 2013


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:

        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
        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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