{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:43:54Z","timestamp":1777499034780,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Provincial Science and Technology Development Plan Project","award":["20220203184SF"],"award-info":[{"award-number":["20220203184SF"]}]},{"name":"Jilin Provincial Science and Technology Development Plan Project","award":["[2023]051"],"award-info":[{"award-number":["[2023]051"]}]},{"name":"General Project of Graduate Innovation Program","award":["20220203184SF"],"award-info":[{"award-number":["20220203184SF"]}]},{"name":"General Project of Graduate Innovation Program","award":["[2023]051"],"award-info":[{"award-number":["[2023]051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Pareto dominance-based algorithms face a significant challenge in handling many-objective optimization problems. As the number of objectives increases, the sharp rise in non-dominated individuals makes it challenging for the algorithm to differentiate their quality, resulting in a loss of selection pressure. The application of the penalty-based boundary intersection (PBI) method can balance convergence and diversity in algorithms. The PBI method guides the evolution of individuals by integrating the parallel and perpendicular distances between individuals and reference vectors, where the penalty factor is crucial for balancing these two distances and significantly affects algorithm performance. Therefore, a comprehensive adaptive penalty scheme was proposed and applied to NSGA-III, named caps-NSGA-III, to achieve balance and symmetry between convergence and diversity. Initially, each reference vector\u2019s penalty factor is computed based on its own characteristic. Then, during the iteration process, the penalty factor is adaptively adjusted according to the evolutionary state of the individuals associated with the corresponding reference vector. Finally, a monitoring strategy is designed to oversee the penalty factor, ensuring that adaptive adjustments align with the algorithm\u2019s needs at different stages. Through a comparison involving benchmark experiments and two real-world problems, the competitiveness of caps-NSGA-III was demonstrated.<\/jats:p>","DOI":"10.3390\/sym16101289","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T07:41:49Z","timestamp":1727768509000},"page":"1289","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Improved NSGA-III with a Comprehensive Adaptive Penalty Scheme for Many-Objective Optimization"],"prefix":"10.3390","volume":"16","author":[{"given":"Xinghang","family":"Xu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Beihua University, Jilin 132013, China"}]},{"given":"Du","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun 130012, China"}]},{"given":"Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Beihua University, Jilin 132013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6541-9916","authenticated-orcid":false,"given":"Qingliang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China"}]},{"given":"Fanhua","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. 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