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A new algorithm for the problem of robust single objective optimization

dc.contributor.authorNoriega González, Álvaro 
dc.contributor.authorVijande Díaz, Ricardo 
dc.contributor.authorRodríguez Ordóñez, Eduardo 
dc.contributor.authorCortizo Rodríguez, José Luis 
dc.contributor.authorSierra Velasco, José Manuel
dc.identifier.citationInt. J. Simul. Multidisci. Des. Optim., 2, p.223-229 (2008); doi:10.1051/ijsmdo:2008030spa
dc.description.abstractThis paper propounds a new algorithm, the Sub-Space Random Search (SSRS) for the problem of singleobjective 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 Deceptivebimodal (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.en
dc.format.extentp. 223-229spa
dc.publisherEDP Sciences
dc.relation.ispartofInternational Journal for Simulation and Multidisciplinary Design Optimizationen
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 3.0 España
dc.subjectUnconstrained Optimizationen
dc.subjectStratified Random Searchen
dc.subjectSynthesis of Mechanismsen
dc.titleA new algorithm for the problem of robust single objective optimizationen

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CC Reconocimiento - No comercial - Sin obras derivadas 3.0 España
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