A new algorithm for the problem of robust single objective optimization
This paper propounds a new algorithm, the Sub-Space Random Search (SSRS) for the problem of single-objective optimization, with the aim of improving the robustness and the precision of classical methods of global optimization. The new algorithm is compared with a genetic algorithm (GA), on a set...
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doaj-85e320f198f047debf6b527bc777de682021-02-02T05:07:48ZengEDP SciencesInternational Journal for Simulation and Multidisciplinary Design Optimization1779-627X1779-62882008-07-012322322910.1051/ijsmdo:2008030asmdo4008A new algorithm for the problem of robust single objective optimizationNoriega A.Vijande R.Rodríguez E.Cortizo J. L.Sierra J. M.This paper propounds a new algorithm, the Sub-Space Random Search (SSRS) for the problem of single-objective optimization, with the aim of improving the robustness and the precision of classical methods of global optimization. The new algorithm is compared with a genetic algorithm (GA), on a set of four scaleable test functions and with the number of variables changing from 1 to 5. A new test function called Deceptive-bimodal (DB) is proposed. Results indicate that, with the same total number of function evaluations, SSRS is about 50% faster than GA. Moreover, SSRS shows a greater precision and similar ability to find the global optimum than GA with 1, 2 and sometimes 3 variables. But this advantage diminishes when the number of variables increases on multimodal and narrow-flat valley functions. Finally, SSRS is successfully applied to a problem of dynamical synthesis of a mechanism.https://www.ijsmdo.org/articles/smdo/pdf/2008/03/asmdo4008.pdfmeta-heuristicunconstrained optimizationstratified random searchsynthesis of mechanisms. |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Noriega A. Vijande R. Rodríguez E. Cortizo J. L. Sierra J. M. |
spellingShingle |
Noriega A. Vijande R. Rodríguez E. Cortizo J. L. Sierra J. M. A new algorithm for the problem of robust single objective optimization International Journal for Simulation and Multidisciplinary Design Optimization meta-heuristic unconstrained optimization stratified random search synthesis of mechanisms. |
author_facet |
Noriega A. Vijande R. Rodríguez E. Cortizo J. L. Sierra J. M. |
author_sort |
Noriega A. |
title |
A new algorithm for the problem of robust single objective
optimization |
title_short |
A new algorithm for the problem of robust single objective
optimization |
title_full |
A new algorithm for the problem of robust single objective
optimization |
title_fullStr |
A new algorithm for the problem of robust single objective
optimization |
title_full_unstemmed |
A new algorithm for the problem of robust single objective
optimization |
title_sort |
new algorithm for the problem of robust single objective
optimization |
publisher |
EDP Sciences |
series |
International Journal for Simulation and Multidisciplinary Design Optimization |
issn |
1779-627X 1779-6288 |
publishDate |
2008-07-01 |
description |
This paper propounds a new algorithm, the Sub-Space Random Search (SSRS) for
the problem of single-objective optimization, with the aim of improving the
robustness and the precision of classical methods of global optimization.
The new algorithm is compared with a genetic algorithm (GA), on a set of
four scaleable test functions and with the number of variables changing from
1 to 5. A new test function called Deceptive-bimodal (DB) is proposed.
Results indicate that, with the same total number of function evaluations,
SSRS is about 50% faster than GA. Moreover, SSRS shows a greater
precision and similar ability to find the global optimum than GA with 1, 2
and sometimes 3 variables. But this advantage diminishes when the number of
variables increases on multimodal and narrow-flat valley functions. Finally,
SSRS is successfully applied to a problem of dynamical synthesis of a
mechanism. |
topic |
meta-heuristic unconstrained optimization stratified random search synthesis of mechanisms. |
url |
https://www.ijsmdo.org/articles/smdo/pdf/2008/03/asmdo4008.pdf |
work_keys_str_mv |
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