The average running time of all competing algorithms.
<p>The average running time of all competing algorithms.</p>
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2025
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| _version_ | 1852020309811527680 |
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| author | Genliang Li (5816264) |
| author2 | Yaxin Cui (16850040) Jingyu Su (2522416) |
| author2_role | author author |
| author_facet | Genliang Li (5816264) Yaxin Cui (16850040) Jingyu Su (2522416) |
| author_role | author |
| dc.creator.none.fl_str_mv | Genliang Li (5816264) Yaxin Cui (16850040) Jingyu Su (2522416) |
| dc.date.none.fl_str_mv | 2025-05-16T17:37:05Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0318903.g007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_average_running_time_of_all_competing_algorithms_/29089518 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified minimal parameter requirements grey wolf optimizer enhance search efficiency prevent premature convergence feature subset size global search capability heuristic algorithm rooted dimensional classification problems gwo &# 8217 proposed amgwo method dimensional classification search process global optimum feature selection fast convergence &# 160 classification accuracy widely used thus confirming thereby preventing thereby enhancing swarm intelligence potential solutions original gwo machine learning local optima known meta irrelevant features getting trapped exploitation effectively execution speed eliminate redundant effectively find data mining crucial component converging prematurely balance exploration approach encompasses allowing amgwo adaptive mechanism |
| dc.title.none.fl_str_mv | The average running time of all competing algorithms. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The average running time of all competing algorithms.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_02bda280697c4ebec1d95ffd334f3392 |
| identifier_str_mv | 10.1371/journal.pone.0318903.g007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29089518 |
| 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 average running time of all competing algorithms.Genliang Li (5816264)Yaxin Cui (16850040)Jingyu Su (2522416)Biological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedminimal parameter requirementsgrey wolf optimizerenhance search efficiencyprevent premature convergencefeature subset sizeglobal search capabilityheuristic algorithm rooteddimensional classification problemsgwo &# 8217proposed amgwo methoddimensional classificationsearch processglobal optimumfeature selectionfast convergence&# 160classification accuracywidely usedthus confirmingthereby preventingthereby enhancingswarm intelligencepotential solutionsoriginal gwomachine learninglocal optimaknown metairrelevant featuresgetting trappedexploitation effectivelyexecution speedeliminate redundanteffectively finddata miningcrucial componentconverging prematurelybalance explorationapproach encompassesallowing amgwoadaptive mechanism<p>The average running time of all competing algorithms.</p>2025-05-16T17:37:05ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0318903.g007https://figshare.com/articles/figure/The_average_running_time_of_all_competing_algorithms_/29089518CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290895182025-05-16T17:37:05Z |
| spellingShingle | The average running time of all competing algorithms. Genliang Li (5816264) Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified minimal parameter requirements grey wolf optimizer enhance search efficiency prevent premature convergence feature subset size global search capability heuristic algorithm rooted dimensional classification problems gwo &# 8217 proposed amgwo method dimensional classification search process global optimum feature selection fast convergence &# 160 classification accuracy widely used thus confirming thereby preventing thereby enhancing swarm intelligence potential solutions original gwo machine learning local optima known meta irrelevant features getting trapped exploitation effectively execution speed eliminate redundant effectively find data mining crucial component converging prematurely balance exploration approach encompasses allowing amgwo adaptive mechanism |
| status_str | publishedVersion |
| title | The average running time of all competing algorithms. |
| title_full | The average running time of all competing algorithms. |
| title_fullStr | The average running time of all competing algorithms. |
| title_full_unstemmed | The average running time of all competing algorithms. |
| title_short | The average running time of all competing algorithms. |
| title_sort | The average running time of all competing algorithms. |
| topic | Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified minimal parameter requirements grey wolf optimizer enhance search efficiency prevent premature convergence feature subset size global search capability heuristic algorithm rooted dimensional classification problems gwo &# 8217 proposed amgwo method dimensional classification search process global optimum feature selection fast convergence &# 160 classification accuracy widely used thus confirming thereby preventing thereby enhancing swarm intelligence potential solutions original gwo machine learning local optima known meta irrelevant features getting trapped exploitation effectively execution speed eliminate redundant effectively find data mining crucial component converging prematurely balance exploration approach encompasses allowing amgwo adaptive mechanism |