Online from: 1929
Subject Area: Mechanical & Materials Engineering
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|Title:||Vibrational genetic algorithm enhanced with neural networks in RCS problems|
|Author(s):||Y. Volkan Pehlivanoglu, (Turkish Air Force Academy, Istanbul, Turkey), Oktay Baysal, (Batten College of Engineering and Technology, Old Dominion University, Norfolk, Virginia, USA)|
|Citation:||Y. Volkan Pehlivanoglu, Oktay Baysal, (2011) "Vibrational genetic algorithm enhanced with neural networks in RCS problems", Aircraft Engineering and Aerospace Technology, Vol. 83 Iss: 1, pp.43 - 48|
|Keywords:||Genetic algorithms, Neural nets, Optimization techniques, Radar|
|Article type:||Research paper|
|DOI:||10.1108/00022661111119919 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
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 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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