Vibrational genetic algorithm enhanced with neural networks in RCS problems

Abstract

PurposeThe 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/approachThe 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.FindingsFor the demonstration problem considered, remarkable reduction in the computational time has been accomplished.Research limitations/implicationsThe results reported in this paper suggest an efficient GA optimization methodology for engineering problems.Originality/valueOwing to reduction in computational time, the new method provides a shorter design cycle for engineering problems.