Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Abstract

Deterministic methods are used for optimum solution of many engineering and scientific problems. Filled function method, which is a deterministic method, is not trapped to local minimums with its ability to bypass energy barriers. In order to achieve this, the basin regions of the filled function should be located, and the filled function should be constructed in that region. However, classical search strategies used for finding the basin regions don't yield effective results. In this study, a new stochastic search approach is presented as a faster and more efficient alternative to classic filled function search strategy. An unconstrained global optimization method based on clustering and parabolic approximation (GOBC-PA) has been used as a stochastic method for accelerating the L type filled function as a deterministic method. The developed method has been tested against classical filled function using 11 benchmark functions. When the obtained results are examined, it is seen that the stochastic search approach has superiority over the mean error, standard deviation and elapsed time values according to the classical approach. These results show that the combination of deterministic and stochastic methods can be more successful in finding the global minimum against the classic deterministic method.