TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems.
Large-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. This paper proposes an improved...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314584 |
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| author | Xiaozhi Du Kai Chen Hongyuan Du Zongbin Qiao |
| author_facet | Xiaozhi Du Kai Chen Hongyuan Du Zongbin Qiao |
| author_sort | Xiaozhi Du |
| collection | DOAJ |
| description | Large-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. This paper proposes an improved sparrow search algorithm (SSA) that manages convergence and diversity separately for solving LSMaOPs, called two-stage sparrow search algorithm (TS-SSA). In the first stage of TS-SSA, this paper proposes a many-objective sparrow search algorithm (MaOSSA) to mainly manages the convergence through the adaptive population dividing strategy and the random bootstrap search strategy. In the second stage of TS-SSA, this paper proposes a dynamic multi-population search strategy to mainly manage the diversity of the population through the dynamic population dividing strategy and the multi-population search strategy. TS-SSA has been experimentally compared with 10 state-of-the-art MOEAs on DTLZ and LSMOP benchmark test problems with 3-20 objectives and 300-2000 decision variables. The results show that TS-SSA has significant performance and efficiency advantages in solving LSMaOPs. In addition, we apply TS-SSA to a real case (automatic test scenarios generation), and the result shows that TS-SSA outperforms other algorithms on diversity. |
| format | Article |
| id | doaj-art-ee7d60cd321c4196b8f7a2ef8d923625 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ee7d60cd321c4196b8f7a2ef8d9236252025-08-20T02:33:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031458410.1371/journal.pone.0314584TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems.Xiaozhi DuKai ChenHongyuan DuZongbin QiaoLarge-scale many-objective optimization problems (LSMaOPs) are a current research hotspot. However, since LSMaOPs involves a large number of variables and objectives, state-of-the-art methods face a huge search space, which is difficult to be explored comprehensively. This paper proposes an improved sparrow search algorithm (SSA) that manages convergence and diversity separately for solving LSMaOPs, called two-stage sparrow search algorithm (TS-SSA). In the first stage of TS-SSA, this paper proposes a many-objective sparrow search algorithm (MaOSSA) to mainly manages the convergence through the adaptive population dividing strategy and the random bootstrap search strategy. In the second stage of TS-SSA, this paper proposes a dynamic multi-population search strategy to mainly manage the diversity of the population through the dynamic population dividing strategy and the multi-population search strategy. TS-SSA has been experimentally compared with 10 state-of-the-art MOEAs on DTLZ and LSMOP benchmark test problems with 3-20 objectives and 300-2000 decision variables. The results show that TS-SSA has significant performance and efficiency advantages in solving LSMaOPs. In addition, we apply TS-SSA to a real case (automatic test scenarios generation), and the result shows that TS-SSA outperforms other algorithms on diversity.https://doi.org/10.1371/journal.pone.0314584 |
| spellingShingle | Xiaozhi Du Kai Chen Hongyuan Du Zongbin Qiao TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. PLoS ONE |
| title | TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. |
| title_full | TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. |
| title_fullStr | TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. |
| title_full_unstemmed | TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. |
| title_short | TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems. |
| title_sort | ts ssa an improved two stage sparrow search algorithm for large scale many objective optimization problems |
| url | https://doi.org/10.1371/journal.pone.0314584 |
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