A novel filled function for unconstrained global optimization

In this paper, we present a novel filled function approach for finding better minimizer of an unconstrained global optimization. The proposed new filled function contains no parameters, it has more advantages over those with parameters and have wide applications in real world life. Moreover, the iterations process in the proposed filled function algorithm can be easily actualized. we also make a numerical test to demonstrate the efficiency of the proposed approach. The numerical results show that our filled function approach is efficient and reliable.