The structure of a three-bar truss.
<div><p>Whale Optimization Algorithm (WOA) suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. Thus, an enhanced Whale Optimiz...
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2025
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| _version_ | 1852017025356922880 |
|---|---|
| author | Junhao Wei (6816803) |
| author2 | Yanzhao Gu (21192659) Zhanxi Xie (22177279) Yuzheng Yan (22177282) Baili Lu (21192662) Zikun Li (2460040) Ngai Cheong (21192665) Jiafeng Zhang (233021) Song Zhang (180477) |
| author2_role | author author author author author author author author |
| author_facet | Junhao Wei (6816803) Yanzhao Gu (21192659) Zhanxi Xie (22177279) Yuzheng Yan (22177282) Baili Lu (21192662) Zikun Li (2460040) Ngai Cheong (21192665) Jiafeng Zhang (233021) Song Zhang (180477) |
| author_role | author |
| dc.creator.none.fl_str_mv | Junhao Wei (6816803) Yanzhao Gu (21192659) Zhanxi Xie (22177279) Yuzheng Yan (22177282) Baili Lu (21192662) Zikun Li (2460040) Ngai Cheong (21192665) Jiafeng Zhang (233021) Song Zhang (180477) |
| dc.date.none.fl_str_mv | 2025-09-03T17:33:05Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0322058.g020 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_structure_of_a_three-bar_truss_/30044728 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified outstanding metaheuristic algorithms low population diversity escape local optima algorithm &# 8217 slow convergence rate low convergence accuracy improving convergence speed spiral shape parameters experimental results show excellent woa variants premature convergence results demonstrate experimental section spiral flight enhanced spiral wide range various dimensions quality baseline performs excellently optimal solution multiple strategies levy flight later stages innovative strategies dynamically adjusting different stages different dimensions canonical woa benchmark functions application scenarios accurately updating |
| dc.title.none.fl_str_mv | The structure of a three-bar truss. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Whale Optimization Algorithm (WOA) suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. Thus, an enhanced Whale Optimization Algorithm (LSWOA) based on multiple strategies is proposed, aiming to overcome the limitations of the canonical WOA. The performance of the canonical WOA is improved through innovative strategies: first, an initialization process using Good Nodes Set is introduced to ensure that the search starts from a higher-quality baseline; second, a distance-based guided search strategy is employed to adjust the search direction and intensity by calculating the distance to the optimal solution, which enhances the algorithm’s ability to escape local optima; and lastly, LSWOA introduces an enhanced spiral updating strategy, while the enhanced spiral-enveloping prey strategy effectively balances exploration and exploitation by dynamically adjusting the spiral shape parameters to adapt to different stages of the search, thereby more accurately updating the positions of individuals and improving convergence speed. In the experimental section, we validate the efficiency and superiority of LSWOA by comparing it with outstanding metaheuristic algorithms and excellent WOA variants. The experimental results show that LSWOA exhibits significant optimization performance on the benchmark functions with various dimensions. Additionally, LSWOA is tested on seven engineering design optimization problems, and the results demonstrate that it performs excellently in these application scenarios, effectively solving complex optimization problems in different dimensions and showing its potential for a wide range of applications in real-world engineering challenges.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_628d6d94eba49f9f598a042cbeed2f8a |
| identifier_str_mv | 10.1371/journal.pone.0322058.g020 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30044728 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The structure of a three-bar truss.Junhao Wei (6816803)Yanzhao Gu (21192659)Zhanxi Xie (22177279)Yuzheng Yan (22177282)Baili Lu (21192662)Zikun Li (2460040)Ngai Cheong (21192665)Jiafeng Zhang (233021)Song Zhang (180477)Space ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedoutstanding metaheuristic algorithmslow population diversityescape local optimaalgorithm &# 8217slow convergence ratelow convergence accuracyimproving convergence speedspiral shape parametersexperimental results showexcellent woa variantspremature convergenceresults demonstrateexperimental sectionspiral flightenhanced spiralwide rangevarious dimensionsquality baselineperforms excellentlyoptimal solutionmultiple strategieslevy flightlater stagesinnovative strategiesdynamically adjustingdifferent stagesdifferent dimensionscanonical woabenchmark functionsapplication scenariosaccurately updating<div><p>Whale Optimization Algorithm (WOA) suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. Thus, an enhanced Whale Optimization Algorithm (LSWOA) based on multiple strategies is proposed, aiming to overcome the limitations of the canonical WOA. The performance of the canonical WOA is improved through innovative strategies: first, an initialization process using Good Nodes Set is introduced to ensure that the search starts from a higher-quality baseline; second, a distance-based guided search strategy is employed to adjust the search direction and intensity by calculating the distance to the optimal solution, which enhances the algorithm’s ability to escape local optima; and lastly, LSWOA introduces an enhanced spiral updating strategy, while the enhanced spiral-enveloping prey strategy effectively balances exploration and exploitation by dynamically adjusting the spiral shape parameters to adapt to different stages of the search, thereby more accurately updating the positions of individuals and improving convergence speed. In the experimental section, we validate the efficiency and superiority of LSWOA by comparing it with outstanding metaheuristic algorithms and excellent WOA variants. The experimental results show that LSWOA exhibits significant optimization performance on the benchmark functions with various dimensions. Additionally, LSWOA is tested on seven engineering design optimization problems, and the results demonstrate that it performs excellently in these application scenarios, effectively solving complex optimization problems in different dimensions and showing its potential for a wide range of applications in real-world engineering challenges.</p></div>2025-09-03T17:33:05ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0322058.g020https://figshare.com/articles/figure/The_structure_of_a_three-bar_truss_/30044728CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300447282025-09-03T17:33:05Z |
| spellingShingle | The structure of a three-bar truss. Junhao Wei (6816803) Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified outstanding metaheuristic algorithms low population diversity escape local optima algorithm &# 8217 slow convergence rate low convergence accuracy improving convergence speed spiral shape parameters experimental results show excellent woa variants premature convergence results demonstrate experimental section spiral flight enhanced spiral wide range various dimensions quality baseline performs excellently optimal solution multiple strategies levy flight later stages innovative strategies dynamically adjusting different stages different dimensions canonical woa benchmark functions application scenarios accurately updating |
| status_str | publishedVersion |
| title | The structure of a three-bar truss. |
| title_full | The structure of a three-bar truss. |
| title_fullStr | The structure of a three-bar truss. |
| title_full_unstemmed | The structure of a three-bar truss. |
| title_short | The structure of a three-bar truss. |
| title_sort | The structure of a three-bar truss. |
| topic | Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified outstanding metaheuristic algorithms low population diversity escape local optima algorithm &# 8217 slow convergence rate low convergence accuracy improving convergence speed spiral shape parameters experimental results show excellent woa variants premature convergence results demonstrate experimental section spiral flight enhanced spiral wide range various dimensions quality baseline performs excellently optimal solution multiple strategies levy flight later stages innovative strategies dynamically adjusting different stages different dimensions canonical woa benchmark functions application scenarios accurately updating |