Int. J. Simul. Multidisci. Des. Optim.
Volume 2, Number 3, July 2008
|Page(s)||223 - 229|
|Published online||09 December 2008|
A new algorithm for the problem of robust single objective optimization
Department of Mechanical Engineering, University of Oviedo, Gijón, Spain
Corresponding author: firstname.lastname@example.org
Accepted: 15 February 2008
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.
Key words: Meta-heuristic / unconstrained optimization / stratified random search / synthesis of mechanisms.
© ASMDO, EDP Sciences, 2008
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