1 |
E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, Gsa: a gravitational search algorithm, Inf. Sci. 179 (13) (2009) 2232-2248.
DOI
|
2 |
L. Bouchet, A comparative study of deconvolution methods for gamma-ray spectra, Astron. AstroPhys. Suppl. 113 (1995) 167-183.
|
3 |
M.J. Sasena, P. Papalambros, P. Goovaerts, Exploration of metamodeling sampling criteria for constrained global optimization, Eng. Optim. 34 (3) (2002) 263-278.
DOI
|
4 |
G. Gilmore, Practical Gamma-Ray Spectroscopy, John Wiley & Sons, 2011.
|
5 |
N. Tsoulfanidis, S. Landsberger, Measurement and Detection of Radiation, CRC press, 2015.
|
6 |
Z.W. Geem, Music-inspired Harmony Search Algorithm: Theory and Applications, vol. 191, Springer, 2009.
|
7 |
M. Dorigo, T. Stutzle, Ant colony optimization: overview and recent advances, Techreport, IRIDIA, Universite Libre de Bruxelles 8.
|
8 |
F. Glover, M. Laguna, Tabu Search, John Wiley & Sons, Inc., 1993.
|
9 |
F. Ramezani, S. Lotfi, Social-based algorithm (sba), Appl. Soft Comput. 13 (5) (2013) 2837-2856.
DOI
|
10 |
D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput. 1 (1) (1997) 67-82.
DOI
|
11 |
S. Ashrafi, O. Jahanbakhsh, D. Alizadeh, B. Salehpour, A novel method for nondestructive compton scatter imaging based on the genetic algorithm, Cent. Eur. J. Phys. 11 (5) (2013) 560-567.
|
12 |
J. Lilley, Nuclear Physics: Principles and Applications, John Wiley & Sons, 2013.
|
13 |
J. Nocedal, S. Wright, Numerical Optimization, Springer Science & Business Media, 2006.
|
14 |
A. Taheri, Jinhwan Kim, Kyeongjin Park, Gyuseong Cho, Multi-radioisotope identification algorithm using an artificial neural network for plastic gamma spectra, Appl. Radiat. Isot. 147 (2019) 83-90.
DOI
|
15 |
S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software 95 (2016) 51-67.
DOI
|
16 |
D. Simon, Biogeography-based optimization, IEEE Trans. Evol. Comput. 12 (6) (2008) 702-713.
DOI
|
17 |
R.A. Formato, Central force optimization, Prog Electromagn Res 77 (2007) 425-491.
DOI
|
18 |
W.R. Leo, Techniques for Nuclear and Particles Physics Experimentsa Howto Approach, 2nd Revised Edition, Springer-Verlag, Berlin, 1994.
|
19 |
G.F. Knoll, Radiation Detection and Measurement, John Wiley & Sons, 2010.
|
20 |
T. Kennett, W. Prestwich, A. Robertson, Bayesian deconvolution i: convergent properties, Nucl. Instrum. Methods 151 (1-2) (1978) 285-292.
DOI
|
21 |
D. Diver, D. Ireland, Spectral decomposition by genetic algorithm, Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 399 (2-3) (1997) 414-420.
DOI
|
22 |
X.-S. Yang, Engineering Optimization: an Introduction with Metaheuristic Applications, John Wiley & Sons, 2010.
|
23 |
J. Chen, B. Xin, Z. Peng, L. Dou, J. Zhang, Optimal contraction theorem for exploration{exploitation tradeoff in search and optimization, IEEE Trans. Syst. Man Cybern. Syst. Hum. 39 (3) (2009) 680-691.
DOI
|
24 |
S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Software 69 (2014) 46-61.
DOI
|
25 |
X.-S. Yang, Firefly algorithm, Nature-inspired metaheuristic algorithms 20 (2008) 79-90.
|
26 |
R.V. Rao, V.J. Savsani, D. Vakharia, Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems, Inf. Sci. 183 (1) (2012) 1-15.
DOI
|
27 |
E.-G. Talbi, Metaheuristics: from Design to Implementation, vol. 74, John Wiley & Sons, 2009.
|
28 |
L. Bianchi, M. Dorigo, L.M. Gambardella, W.J. Gutjahr, A survey on metaheuristics for stochastic combinatorial optimization, Nat. Comput. 8 (2) (2009) 239-287.
DOI
|
29 |
J.G. Digalakis, K.G. Margaritis, On benchmarking functions for genetic algorithms, Int. J. Comput. Math. 77 (4) (2001) 481-506.
DOI
|
30 |
E. Alba, B. Dorronsoro, The exploration/exploitation tradeoff in dynamic cellular genetic algorithms, IEEE Trans. Evol. Comput. 9 (2) (2005) 126-142.
DOI
|
31 |
J. Momin, X.-S. Yang, A literature survey of benchmark functions for global optimization problems, Journal of Mathematical Modelling and Numerical Optimisation 4 (2) (2013) 150-194.
DOI
|
32 |
S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing, Science 220 (4598) (1983) 671-680.
DOI
|
33 |
F.W. Glover, G.A. Kochenberger, Handbook of Metaheuristics, vol. 57, Springer Science & Business Media, 2006.
|
34 |
J.H. Holland, Genetic algorithms, Sci. Am. 267 (1) (1992) 66-73.
DOI
|
35 |
I. Rechenberg, Evolution strategy: natures way of optimization. Optimization: Methods and Applications, Possibilities and Limitations, Springer, 1989, pp. 106-126.
|
36 |
A. Kaveh, S. Talatahari, A novel heuristic optimization method: charged system search, Acta Mech. 213 (3-4) (2010) 267-289.
DOI
|
37 |
R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization, Swarm intelligence 1 (1) (2007) 33-57.
DOI
|
38 |
P. Bandzuch, M. Morhac, J. Kristiak, Study of the van cittert and gold iterative methods of deconvolution and their application in the deconvolution of experimental spectra of positron annihilation, Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 384 (2-3) (1997) 506-515.
DOI
|
39 |
T. Burr, Michael Hamada, Radio-isotope identification algorithms for NaI g spectra, Algorithms 2 (1) (2009) 339-360.
DOI
|
40 |
M. Mariscotti, A method for automatic identification of peaks in the presence of background and its application to spectrum analysis, Nucl. Instrum. Methods 50 (2) (1967) 309-320.
DOI
|
41 |
L.J. Meng, D. Ramsden, An inter-comparison of three spectraldeconvolution algorithms for gamma-ray spectroscopy, IEEE Trans. Nucl. Sci. 47 (4) (2000) 1329-1336.
DOI
|
42 |
C. Carlevaro, M. Wilkinson, L. Barrios, A genetic algorithm approach to routine gamma spectra analysis, J. Instrum. 3 (2008) P01001, 01.
|