Vibrational genetic algorithm enhanced with neural networks in RCS problems

Abstract

Purpose

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.

Citation

Volkan Pehlivanoglu, Y. and Baysal, O. (2011), "Vibrational genetic algorithm enhanced with neural networks in RCS problems", Aircraft Engineering and Aerospace Technology, Vol. 83 No. 1, pp. 43-48. https://doi.org/10.1108/00022661111119919