Harmonic competition

A self-organizing multiple criteria optimization

Yasuo Matsuyama

    Research output: Contribution to journalArticle

    25 Citations (Scopus)

    Abstract

    Harmonic competition is a learning strategy based upon winner-take-all or winner-take-quota with respect to a composite of heterogeneous subcosts. This learning is unsupervised and organizes itself. The subcosts may conflict with each other. Thus, the total learning system realizes a self-organizing multiple criteria optimization. The subcosts are combined additively and multiplicatively using adjusting parameters. For such a total cost, a general successive learning algorithm is derived first. Then, specific problems in the Euclidian space are addressed. Vector quantization with various constraints and traveling salesperson problems are selected as test problems. The former is a typical class of problems where the number of neurons is less than that of the data. The latter is an opposite case. Duality exists in these two classes. In both cases, the combination parameters of the subcosts show wide dynamic ranges in the course of learning. It is possible, however, to decide the parameter control from the structure of the total cost. This method finds a preferred solution from the Pareto optimal set of the multiple object optimization. Controlled mutations motivated by genetic algorithms are proved to be effective in finding near-optimal solutions. All results show significance of the additional constraints and the effectiveness of the dynamic parameter control.

    Original languageEnglish
    Pages (from-to)652-668
    Number of pages17
    JournalIEEE Transactions on Neural Networks
    Volume7
    Issue number3
    DOIs
    Publication statusPublished - 1996

    Fingerprint

    Multiple Criteria Optimization
    Self-organizing
    Harmonic
    Control Parameter
    Unsupervised learning
    Vector quantization
    Learning algorithms
    Winner-take-all
    Neurons
    Learning systems
    Costs
    Learning Strategies
    Vector Quantization
    Genetic algorithms
    Dynamic Range
    Learning Systems
    Test Problems
    Learning Algorithm
    Neuron
    Composite materials

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Theoretical Computer Science
    • Electrical and Electronic Engineering
    • Artificial Intelligence
    • Computational Theory and Mathematics
    • Hardware and Architecture

    Cite this

    Harmonic competition : A self-organizing multiple criteria optimization. / Matsuyama, Yasuo.

    In: IEEE Transactions on Neural Networks, Vol. 7, No. 3, 1996, p. 652-668.

    Research output: Contribution to journalArticle

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