Evolutionary Computation for Sparse Multi-Objective Optimization: A Survey
Shuai Shao, Ye Tian, Yajie Zhang, Shangshang Yang, Panpan Zhang, Cheng He, Xingyi Zhang, Yaochu Jin · ACM Computing Surveys · 2025
In various scientific and engineering domains, optimization problems often feature multiple objectives and sparse optimal solutions, which are commonly known as sparse multi-objective optimization problems (SMOPs). Since many SMOPs are pursued based on large datasets, they involve a large number of decision variables, leading to a huge search space that is challenging to find sparse Pareto optimal solutions. To address this issue, a number of multi-objective evolutionary algorithms (MOEAs) have been developed in recent years to identify non-zero variables through novel search strategies. However, there is currently limited literature that systematically reviews the related studies. In this article, a comprehensive survey is presented for sparse multi-objective optimization, which starts with a definition of SMOPs, followed by a taxonomy of existing sparse MOEAs. Then, the sparse MOEAs are reviewed in detail, followed by an introduction of benchmark and real-world applications that are used for performance assessment in sparse optimization. Finally, the survey is finished by summarizing the research status of sparse multi-objective optimization and outlining some promising research directions.