Vibrational genetic algorithm enhanced with neural networks in RCS problems

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Vibrational genetic algorithm enhanced with neural networks in RCS problems

Volkan Pehlivanoglu, Y. / Baysal, Oktay

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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 VGA^m) 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.