Combining filter method and dynamically dimensioned search for constrained global optimization

• Main Author: Macêdo, M. Joseane F. G.
• Publication Date: 2017
• Other Authors: Costa, M. Fernanda P., Rocha, Ana Maria A. C., Karas, Elizabeth W.
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/1822/49154
• topic: Global optimization; Dynamically dimensioned search algorithm; Filter methods; Ciências Naturais::Matemáticas; Science & Technology

In this work we present an algorithm that combines the filter technique and the dynamically dimensioned search (DDS) for solving nonlinear and nonconvex constrained global optimization problems. The DDS is a stochastic global algorithm for solving bound constrained problems that in each iteration generates a randomly trial point perturbing some coordinates of the current best point. The filter technique controls the progress related to optimality and feasibility defining a forbidden region of points refused by the algorithm. This region can be given by the flat or slanting filter rule. The proposed algorithm does not compute or approximate any derivatives of the objective and constraint functions. Preliminary experiments show that the proposed algorithm gives competitive results when compared with other methods.