Discovering promising regions to help global numerical optimization algorithms

• Autor(a) principal: Melo, Vinicius V. de
• Data de Publicação: 2007
• Outros Autores: Delbem, Alexandre C. B., Pinto Junior, Dorival L., Federson, Fernando M.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-540-76631-5_8; http://hdl.handle.net/11449/116223

We have developed an algorithm using a Design of Experiments technique for reduction of search-space in global optimization problems. Our approach is called Domain Optimization Algorithm. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. The Domain Optimization Algorithm approach is based on eliminating non-promising search-space regions, which are identifyed using simple models (linear) fitted to the data. Then, we run a global optimization algorithm starting its population inside the promising region. The proposed approach with this heuristic criterion of population initialization has shown relevant results for tests using hard benchmark functions.