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Subsequently, we seek a robust optimal front that concurrently addresses convergence and robustness by employing a non-dominated sorting approach. Furthermore, we propose a precise sampling method and a random grouping mechanism to accurately recover solutions resilient to real noise while ensuring population\u2019s diversity. In the latter stage, we introduce a performance measure that integrates both robustness and convergence to guide the construction of the robust optimal front. Experimental results demonstrate the superiority of the proposed algorithm in terms of both convergence and robustness compared to existing approaches under noisy conditions.<\/jats:p>","DOI":"10.1007\/s40747-025-01822-y","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T02:31:46Z","timestamp":1740709906000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A novel robust multi-objective evolutionary optimization algorithm based on surviving rate"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6363-6370","authenticated-orcid":false,"given":"Wenxiang","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Kai","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Shuwei","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Lihong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"issue":"4","key":"1822_CR1","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/TEVC.2014.2343791","volume":"19","author":"M Asafuddoula","year":"2015","unstructured":"Asafuddoula M, Singh HK, Ray T (2015) Six-sigma robust design optimization using a many-objective decomposition-based evolutionary algorithm. 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