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Surrogate-assisted optimization was developed for handling complex and costly problems, which arise from real-world applications. The main idea behind surrogate-assisted optimization is to optimally exhaust the available information to lower the amount of required expensive function evaluations thus saving time, resources and the related costs. This chapter outlines the existing challenges in this field that include benchmarking, constraint handling, constructing ensembles of surrogates and solving discrete and/or multi-objective optimization problems. We discuss shortcomings of existing techniques, propose suggestions for improvements and give an outlook on promising research directions. This is valuable for practitioners and researchers alike, since the increased availability of computational resources on the one hand and the continuous development of new approaches on the other hand raise …