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Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing

The main goal of this study is to find the most effective set of parameters for the Simplified Generalized Simulated Annealing algorithm, SGSA, when applied to distinct cost function as well as to find a possible correlation between the values of these parameters sets and some topological characteri...

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Main Authors: Dall'Igna Júnior, Alcino, Silva, Renato S., Mundim, Kleber C., Dardenne, Laurent E.
Format: Artigo
Language: English
Published: Sociedade Brasileira de Genética 2017
Subjects:
optimization
generalized simulated annealing
Online Access: http://repositorio.unb.br/handle/10482/26286
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spelling ir-10482-262862019-03-28T14:05:48Z Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing Dall'Igna Júnior, Alcino Silva, Renato S. Mundim, Kleber C. Dardenne, Laurent E. optimization generalized simulated annealing The main goal of this study is to find the most effective set of parameters for the Simplified Generalized Simulated Annealing algorithm, SGSA, when applied to distinct cost function as well as to find a possible correlation between the values of these parameters sets and some topological characteristics of the hypersurface of the respective cost function. The SGSA algorithm is an extended and simplified derivative of the GSA algorithm, a Markovian stochastic process based on Tsallis statistics that has been used in many classes of problems, in particular, in biological molecular systems optimization. In all but one of the studied cost functions, the global minimum was found in 100% of the 50 runs. For these functions the best visiting parameter, qV, belongs to the interval [1.2, 1.7]. Also, the temperature decaying parameter, qT, should be increased when better precision is required. Moreover, the similarity in the locus of optimal parameter sets observed in some functions indicates that possibly one could extract topological information about the cost functions from these sets. 2017-12-07T04:41:35Z 2017-12-07T04:41:35Z 2004 Artigo Genet. Mol. Biol.,v.27,n.4,p.616-622,2004 1415-4757 http://repositorio.unb.br/handle/10482/26286 10.1590/S1415-47572004000400024 en Acesso Aberto application/pdf Sociedade Brasileira de Genética http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572004000400024&lng=en&nrm=iso http://www.scielo.br/scielo.php?script=sci_abstract&pid=S1415-47572004000400024&lng=en&nrm=iso http://www.scielo.br/scielo.php?script=sci_pdf&pid=S1415-47572004000400024&lng=en&nrm=iso
institution REPOSITORIO UNB
collection REPOSITORIO UNB
language English
topic optimization
generalized simulated annealing
spellingShingle optimization
generalized simulated annealing
Dall'Igna Júnior, Alcino
Silva, Renato S.
Mundim, Kleber C.
Dardenne, Laurent E.
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing
description The main goal of this study is to find the most effective set of parameters for the Simplified Generalized Simulated Annealing algorithm, SGSA, when applied to distinct cost function as well as to find a possible correlation between the values of these parameters sets and some topological characteristics of the hypersurface of the respective cost function. The SGSA algorithm is an extended and simplified derivative of the GSA algorithm, a Markovian stochastic process based on Tsallis statistics that has been used in many classes of problems, in particular, in biological molecular systems optimization. In all but one of the studied cost functions, the global minimum was found in 100% of the 50 runs. For these functions the best visiting parameter, qV, belongs to the interval [1.2, 1.7]. Also, the temperature decaying parameter, qT, should be increased when better precision is required. Moreover, the similarity in the locus of optimal parameter sets observed in some functions indicates that possibly one could extract topological information about the cost functions from these sets.
format Artigo
author Dall'Igna Júnior, Alcino
Silva, Renato S.
Mundim, Kleber C.
Dardenne, Laurent E.
author_sort Dall'Igna Júnior, Alcino
title Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing
title_short Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing
title_full Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing
title_fullStr Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing
title_full_unstemmed Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing
title_sort performance and parameterization of the algorithm simplified generalized simulated annealing
publisher Sociedade Brasileira de Genética
publishDate 2017
url http://repositorio.unb.br/handle/10482/26286
_version_ 1641988310332080128
score 13.657419

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