Computational Science Technical Note CSTN-167

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Levy Flights for Particle Swarm Optimisation Algorithms on Graphical Processing Units

A. V. Husselmann and K. A. Hawick

Archived: 2013

Abstract

Particle Swarm Optimisation (PSO) is a powerful algorithm for space search problems such as parametric optimisation. Particles with Levy flights have a long-tailed probability of outlier jumps in the problem space that provide a good compromise between local space exploration and local minima avoidance. Generating many particles and their trajectories with Levy random deviates is computationally expensive, however. We present a data-parallel algorithmic implementation of Levy flighted particle swarm optimisation and show how it makes use of accelerators such as graphical processing units (GPUs). We discuss the computational tradeoffs, performance achievable using GPUs, and the scalability of such an approach using various uni-modal and multi-modal test functions in a range of dimensions.

Keywords: particle swarms; optimisation; multi-modal functions; Levy flights; data parallelism; GPUs

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@ARTICLE{CSTN-167,
        author = {A. V. Husselmann and K. A. Hawick},
        title = {Levy Flights for Particle Swarm Optimisation Algorithms on Graphical
        Processing Units},
        journal = {Parallel and Cloud Computing},
        year = {2013},
        volume = {2},
        pages = {32-40},
        number = {2},
        month = {April},
        institution = {Computer Science, Massey University},
        keywords = {particle swarms; optimisation; multi-modal functions; Levy flights;
        data parallelism; GPUs},
        owner = {dpplayne},
        timestamp = {2015.01.20},
        url = {http://pcc.vkingpub.com/Download.aspx?ID=24}
}


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