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Surv."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3734865","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T11:44:06Z","timestamp":1746791046000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Evolutionary Computation for Sparse Multi-Objective Optimization: A Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5012-2100","authenticated-orcid":false,"given":"Shuai","family":"Shao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3487-5126","authenticated-orcid":false,"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1357-1278","authenticated-orcid":false,"given":"Yajie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0837-5424","authenticated-orcid":false,"given":"Shangshang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-4386","authenticated-orcid":false,"given":"Panpan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi?an University of Technology, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4218-8454","authenticated-orcid":false,"given":"Cheng","family":"He","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-000X","authenticated-orcid":false,"given":"Xingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-0631","authenticated-orcid":false,"given":"Yaochu","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"3082","DOI":"10.37256\/cm.5320242674","article-title":"Generating pareto optimal solutions for multi-objective optimiza tion problems using goal programming","author":"Abdelhamid Alyaa Hegazy","year":"2024","unstructured":"Alyaa Hegazy Abdelhamid, Ramadan Hamed Mohamed, Mahmoud Mostafa Rashwan, and Aya Rezk Allah Farag. 2024. 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