A new algorithm for the problem of robust single objective optimization

• Main Authors: Noriega A., Vijande R., Rodríguez E., Cortizo J. L., Sierra J. M.
• Format: Article
• Language: English
• Published: EDP Sciences 2008-07-01
• Series: International Journal for Simulation and Multidisciplinary Design Optimization
• Subjects: meta-heuristic; unconstrained optimization; stratified random search; synthesis of mechanisms.
• Online Access: https://www.ijsmdo.org/articles/smdo/pdf/2008/03/asmdo4008.pdf
• ISSN: 1779-627X; 1779-6288

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.