Alternative Analysis Networking - A Multi-characterization Algorithm

Kevin Albarado, Roy Hartfield

Abstract

A neural network technique known as unsupervised training was coupled with conventional optimization schemes to develop an optimization scheme which could characterize multiple “optimal” solutions. The tool discussed in this study was developed specifically for the purposes of providing a designer with a method for designing multiple answers to a problem for the purposes of alternative analysis. Discussion of the algorithm is provided along with three example problems: unconstrained 2-dimensional mathematical problem, a tension-compression spring optimization problem, and a solid rocket motor design problem. This algorithm appears to be the first capable of performing the task of finding multiple optimal solutions as efficiently as typical stochastic based optimizers.

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Paper Citation


in Harvard Style

Albarado K. and Hartfield R. (2012). Alternative Analysis Networking - A Multi-characterization Algorithm . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 183-188. DOI: 10.5220/0004147901830188


in Bibtex Style

@conference{ecta12,
author={Kevin Albarado and Roy Hartfield},
title={Alternative Analysis Networking - A Multi-characterization Algorithm},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)},
year={2012},
pages={183-188},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004147901830188},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)
TI - Alternative Analysis Networking - A Multi-characterization Algorithm
SN - 978-989-8565-33-4
AU - Albarado K.
AU - Hartfield R.
PY - 2012
SP - 183
EP - 188
DO - 10.5220/0004147901830188