Convergence Curves of Different Algorithms.
<div><p>In this work, a new Hybrid PSO-Whale Optimization (HPWO) algorithm is introduced to optimize energy consumption in slipform construction. By combining the global exploration power of Particle Swarm Optimization (PSO) with the local exploitation strengths of Whale Optimization Alg...
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| _version_ | 1852014831811428352 |
|---|---|
| author | Wenqin Wang (129400) |
| author2 | Lijun Li (406691) |
| author2_role | author |
| author_facet | Wenqin Wang (129400) Lijun Li (406691) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wenqin Wang (129400) Lijun Li (406691) |
| dc.date.none.fl_str_mv | 2025-11-14T18:37:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0336402.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Convergence_Curves_of_Different_Algorithms_/30623591 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified reducing energy consumption optimize energy consumption local exploitation strengths global exploration power experimental results demonstrate dynamic adjustment mechanisms particle swarm optimization objective optimization model intelligent optimization algorithms new hybrid pso whale optimization algorithm whale optimization xlink "> woa ), vibration systems slipform construction practical solution comprehensive multi |
| dc.title.none.fl_str_mv | Convergence Curves of Different Algorithms. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>In this work, a new Hybrid PSO-Whale Optimization (HPWO) algorithm is introduced to optimize energy consumption in slipform construction. By combining the global exploration power of Particle Swarm Optimization (PSO) with the local exploitation strengths of Whale Optimization Algorithm (WOA), the HPWO algorithm enhances energy management through dynamic adjustment mechanisms. A comprehensive multi-objective optimization model is developed, addressing the interactions between hydraulic, climbing, and vibration systems. Experimental results demonstrate that the HPWO algorithm reduces energy consumption by an average of 18.5%, outperforming traditional optimization methods and offering a practical solution for improving construction efficiency.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_0300a6a2aaefbf825893feb5d8836485 |
| identifier_str_mv | 10.1371/journal.pone.0336402.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30623591 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Convergence Curves of Different Algorithms.Wenqin Wang (129400)Lijun Li (406691)EcologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedPhysical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedreducing energy consumptionoptimize energy consumptionlocal exploitation strengthsglobal exploration powerexperimental results demonstratedynamic adjustment mechanismsparticle swarm optimizationobjective optimization modelintelligent optimization algorithmsnew hybrid psowhale optimization algorithmwhale optimizationxlink ">woa ),vibration systemsslipform constructionpractical solutioncomprehensive multi<div><p>In this work, a new Hybrid PSO-Whale Optimization (HPWO) algorithm is introduced to optimize energy consumption in slipform construction. By combining the global exploration power of Particle Swarm Optimization (PSO) with the local exploitation strengths of Whale Optimization Algorithm (WOA), the HPWO algorithm enhances energy management through dynamic adjustment mechanisms. A comprehensive multi-objective optimization model is developed, addressing the interactions between hydraulic, climbing, and vibration systems. Experimental results demonstrate that the HPWO algorithm reduces energy consumption by an average of 18.5%, outperforming traditional optimization methods and offering a practical solution for improving construction efficiency.</p></div>2025-11-14T18:37:28ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0336402.g002https://figshare.com/articles/figure/Convergence_Curves_of_Different_Algorithms_/30623591CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306235912025-11-14T18:37:28Z |
| spellingShingle | Convergence Curves of Different Algorithms. Wenqin Wang (129400) Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified reducing energy consumption optimize energy consumption local exploitation strengths global exploration power experimental results demonstrate dynamic adjustment mechanisms particle swarm optimization objective optimization model intelligent optimization algorithms new hybrid pso whale optimization algorithm whale optimization xlink "> woa ), vibration systems slipform construction practical solution comprehensive multi |
| status_str | publishedVersion |
| title | Convergence Curves of Different Algorithms. |
| title_full | Convergence Curves of Different Algorithms. |
| title_fullStr | Convergence Curves of Different Algorithms. |
| title_full_unstemmed | Convergence Curves of Different Algorithms. |
| title_short | Convergence Curves of Different Algorithms. |
| title_sort | Convergence Curves of Different Algorithms. |
| topic | Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified reducing energy consumption optimize energy consumption local exploitation strengths global exploration power experimental results demonstrate dynamic adjustment mechanisms particle swarm optimization objective optimization model intelligent optimization algorithms new hybrid pso whale optimization algorithm whale optimization xlink "> woa ), vibration systems slipform construction practical solution comprehensive multi |