Filled Fonksiyonunda Yeni Bir Stokastik Arama Yöntemi

Abstract

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. Searching the basin regions of the filled function is performed by GOBC-PA. The methods used in this study are preferred due to their popularity, speed and robustness. The objective function of the stochastic method is the epsilon value of the gradient that gives the location of basin region. Therefore, the whole purpose of the stochastic method is not to find the global optimum but to find the basin region. The role of finding the global minimum has been left to the deterministic method. The developed method has been tested against classical filled function using 11 benchmark functions and process repeated 10 times. 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.