TY - JOUR
T1 - A comparison of the Covariance Matrix Adaptation Evolution Strategy and the Levenberg-Marquardt method for solving multidimensional inverse transport problems
AU - Bledsoe, Keith C.
AU - Favorite, Jeffrey A.
AU - Aldemir, Tunc
PY - 2011/4
Y1 - 2011/4
N2 - The Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a powerful optimization algorithm that mimics the process of evolution in nature, is applied to the inverse transport problems of interface location identification, source composition identification, and material mass density identification (both separately and combined) in cylindrical radioactive source/shield systems. The energies of discrete gamma-ray lines emitted by the source are assumed to be known, while the uncollided line fluxes are assumed to be measured at points external to the system. CMA-ES is compared to the Levenberg-Marquardt method, a standard gradient-based optimization algorithm, on numerical test cases using both simulated data that is perfectly consistent with the optimization process and with realistic data simulated by Monte Carlo. Numerical results indicate that the Levenberg-Marquardt method is more adept at problems with few unknowns (i.e. ≤3), but as the number of unknowns increases, CMA-ES becomes the superior strategy. Results also indicate that a parallel version of CMA-ES would be more robust than, and have competitive run times with, the Levenberg-Marquardt method for many inverse transport problems.
AB - The Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a powerful optimization algorithm that mimics the process of evolution in nature, is applied to the inverse transport problems of interface location identification, source composition identification, and material mass density identification (both separately and combined) in cylindrical radioactive source/shield systems. The energies of discrete gamma-ray lines emitted by the source are assumed to be known, while the uncollided line fluxes are assumed to be measured at points external to the system. CMA-ES is compared to the Levenberg-Marquardt method, a standard gradient-based optimization algorithm, on numerical test cases using both simulated data that is perfectly consistent with the optimization process and with realistic data simulated by Monte Carlo. Numerical results indicate that the Levenberg-Marquardt method is more adept at problems with few unknowns (i.e. ≤3), but as the number of unknowns increases, CMA-ES becomes the superior strategy. Results also indicate that a parallel version of CMA-ES would be more robust than, and have competitive run times with, the Levenberg-Marquardt method for many inverse transport problems.
KW - Covariance Matrix Adaptation Evolution Strategy
KW - Inverse transport
KW - Levenberg-Marquardt method
KW - Passive gamma rays
UR - http://www.scopus.com/inward/record.url?scp=79151484536&partnerID=8YFLogxK
U2 - 10.1016/j.anucene.2010.09.014
DO - 10.1016/j.anucene.2010.09.014
M3 - Article
AN - SCOPUS:79151484536
SN - 0306-4549
VL - 38
SP - 897
EP - 904
JO - Annals of Nuclear Energy
JF - Annals of Nuclear Energy
IS - 4
ER -