Progress in Computing Applications Tea Tušar 8 1 2018 http://www.dline.info/pca/fulltext/v8n1/pcav8n1_2.pdf Although the motivation to study multiobjective optimization algorithms comes from practice, there are only a few challenging real-world problems freely available to the research community. Because of this, algorithm benchmarking is performed primarily on artificial test problems. The most popular artificial test problems have characteristics that are not well-represented in real-world problems. This and the predominant inadequate performance assessment methodology widen the gap between theory and practice in the eld of multiobjective optimization. The paper suggests to instead compare the algorithms with the anytime performance benchmarking approach of COCO (the Comparing Continuous Optimizers platform) on more realistic artificial problem suites as well as suites with diverse real-world problems. By listing the benets of sharing the real-world problems with the community, the paper hopes to encourage domain experts to embrace this practice.