期刊名称：International Journal of Combinatorial Optimization Problems and Informatics
印刷版ISSN：2007-1558
电子版ISSN：2007-1558
出版年度：2016
卷号：7
期号：3
页码：87-93
语种：English
出版社：International Journal of Combinatorial Optimization Problems and Informatics
其他摘要：One of the main problems of optimization algorithms is that they often end up in a local optimum. It is , therefore , necessary to make sure that the algorithm ge ts out of the local optimum and eventually reaches the global optimum. One of the promising ways guiding one from the local optimum is prompted by the filled function method . It turns out that empirically, the best smoothing functions to use in this method are the Gaussian and Cauchy functions. In this paper, we provide a possible theoretical explanation of this empirical effect.
其他关键词：Optimization Algorithms; Filled Funct ion Method ; Gaussian and Cauchy Functions.
