The purpose of this paper is to develop a new genetic optimization strategy which provides computationally more efficient and accurate solutions, and to provide practically applicable optimization method in radar cross-section (RCS) minimization problems.

    Design/methodology/approach

    The problem of RCS minimization for three-dimensional air vehicle is considered. New computationally efficient optimization tool; neural networks (NNs) coupled multi-frequency vibrational genetic algorithm (NN-coupled VGAm) is based on genetic algorithm (GA) search strategy together with NNs. The results include RCS minimization problem of an air vehicle under structural and aero dynamical-related geometry constraints.

    Findings

    For the demonstration problem considered, remarkable reduction in the computational time has been accomplished.

    Research limitations/implications

    The results reported in this paper suggest an efficient GA optimization methodology for engineering problems.

    Originality/value

    Owing to reduction in computational time, the new method provides a shorter design cycle for engineering problems.


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    Titel :

    Vibrational genetic algorithm enhanced with neural networks in RCS problems


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    25.01.2011


    Format / Umfang :

    6 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch







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