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A new report from Zhengzhou University in China discusses the need for large-scale constrained multi-objective optimization in personalized medicine. The researchers propose a new benchmark for testing algorithms in this field, taking into account realistic features such as mixed linkages between variables and varying numbers of constraint functions. They also introduce a bidirectional sampling strategy to improve algorithm performance in large-scale search spaces with constraints. The proposed algorithm is shown to be effective in solving personalized drug target recognition problems with over 2000 decision variables. [Extracted from the article]