blackboxoptim is intended for computationally expensive black-box global optimization problems with continuous, integer, or mixed-integer variables that are formulated as minimization problems. blackboxoptim can handle single- and multi-objective problems. We call optimization problems “computationally expensive” when objective function evaluations take a considerable amount of time (from several minutes to several hours or more). Such objective function evaluations may require, for example, running a computer simulation and hence the analytical description of the objective function is not available (black box). Furthermore, these objective functions are generally multimodal, i.e. there are several local minima and the goal is to find the global minimum. blackboxoptim contains various surrogate model mixtures, initial experimental design strategies, and sampling strategies. 

In addition to the github download site, please see https://pypi.org/project/blackboxoptim/ for more information.