Glowworm swarm optimisation for training multi-layer perceptrons

Dabiah Ahmed Alboaneen, Huaglory Tianfield, Yan Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    268 Downloads (Pure)

    Abstract

    Training multi-layer perceptron (MLP) is non-trivial due to its non-linear nature and the presence of large number of local optima. Meta-heuristic algorithms may solve this problem efficiently. In this paper, we investigate the use of glowworm swarm optimisation (GSO) algorithm in training the MLP neural network. The GSO based trainer is evaluated on five classification datasets, namely Wisconsin breast cancer, BUPA liver disorders, vertebral column, exclusive OR (XOR) and balloons. The evaluations are conducted by comparing the proposed trainer with four other meta-heuristics, namely biogeography-based optimisation (BBO), genetic algorithm (GA), bat (BAT) and multi-verse optimiser (MVO) algorithms. The results show that our proposed trainer achieves better classification accuracy rate in most datasets compared to the other algorithms.
    Original languageEnglish
    Title of host publicationProceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT '17)
    PublisherIEEE
    Pages131-138
    Number of pages8
    ISBN (Print)9781450355490
    DOIs
    Publication statusPublished - 31 Dec 2017

    Keywords

    • multi-layer perceptrons
    • neural network
    • glowworm swarm optimisation (GSO)

    Fingerprint Dive into the research topics of 'Glowworm swarm optimisation for training multi-layer perceptrons'. Together they form a unique fingerprint.

    Cite this