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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/9140

Title: A new algorithm for the problem of robust single objective optimization
Author(s): Noriega González, Álvaro
Vijande Díaz, Ricardo
Rodríguez Ordóñez, Eduardo
Cortizo Rodríguez, José Luis
Sierra Velasco, José Manuel
Keywords: Meta-Heuristic
Unconstrained Optimization
Stratified Random Search
Synthesis of Mechanisms
Issue date: 2008
Publisher: EDP Sciences
Publisher version: http://dx.doi.org/10.1051/ijsmdo:2008030
Citation: Int. J. Simul. Multidisci. Des. Optim., 2, p.223-229 (2008); doi:10.1051/ijsmdo:2008030
Format extent: p. 223-229
Abstract: This 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.
URI: http://hdl.handle.net/10651/9140
ISSN: 1779-6288
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