Wilcoxon test for MODBO and other MO competitors.
<div><p>Many increasingly complex multi-objective optimization problems are emerging, and there is an urgent need to develop new multi-objective optimization algorithms to meet the challenges. This study introduces the Multi-Objective Dung Beetle Optimization Algorithm (MODBO), which int...
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2025
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| _version_ | 1852015801791414272 |
|---|---|
| author | Wenxing Wu (16853002) |
| author2 | Liqin Tian (802035) Junyi Wu (9381938) Lianhai Lin (17817357) |
| author2_role | author author author |
| author_facet | Wenxing Wu (16853002) Liqin Tian (802035) Junyi Wu (9381938) Lianhai Lin (17817357) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wenxing Wu (16853002) Liqin Tian (802035) Junyi Wu (9381938) Lianhai Lin (17817357) |
| dc.date.none.fl_str_mv | 2025-10-14T17:25:35Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0331713.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Wilcoxon_test_for_MODBO_and_other_MO_competitors_/30357089 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Evolutionary Biology Ecology Developmental Biology Science Policy Computational Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified particles &# 8217 global optimal search generation positionally optimal dominated sorting allows objective optimization algorithms solving complex multi develop new multi objective optimization problems nine algorithms solve multi urgent need study introduces novel algorithm neighborhood mechanisms neighborhood mechanism mops ). modbo algorithm modbo ), integrates competitive improved strategy fast convergence external archive competition mechanism analyze whether |
| dc.title.none.fl_str_mv | Wilcoxon test for MODBO and other MO competitors. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Many increasingly complex multi-objective optimization problems are emerging, and there is an urgent need to develop new multi-objective optimization algorithms to meet the challenges. This study introduces the Multi-Objective Dung Beetle Optimization Algorithm (MODBO), which integrates competitive and neighborhood mechanisms to tackle such problems, Thanks to the dung beetle optimization algorithm’s fast convergence and robust optimization finding ability in single-objective optimization algorithms. The introduction of non-dominated sorting allows the Dung Beetle Optimization Algorithm to solve multi-objective optimization problems (MOPs). To make the Dung Beetle Optimization Algorithm maintain good search ability in searching, we introduce a Competition mechanism to guide the particles’ global optimal search and a Neighborhood mechanism to guide the particles’ local optimal value search. An external archive is introduced to make each generation positionally optimal. Finally, to analyze whether the MODBO algorithm’s improved strategy is effective, a comparison with the nine algorithms on CEC2020 was made, and the 3D sensor deployment problem was used to demonstrate that the MODBO algorithm can solve realistic problems.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_1d5a2c48d45fba0c6b35a5d82ca31cef |
| identifier_str_mv | 10.1371/journal.pone.0331713.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30357089 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Wilcoxon test for MODBO and other MO competitors.Wenxing Wu (16853002)Liqin Tian (802035)Junyi Wu (9381938)Lianhai Lin (17817357)MedicineBiotechnologyEvolutionary BiologyEcologyDevelopmental BiologyScience PolicyComputational BiologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedparticles &# 8217global optimal searchgeneration positionally optimaldominated sorting allowsobjective optimization algorithmssolving complex multidevelop new multiobjective optimization problemsnine algorithmssolve multiurgent needstudy introducesnovel algorithmneighborhood mechanismsneighborhood mechanismmops ).modbo algorithmmodbo ),integrates competitiveimproved strategyfast convergenceexternal archivecompetition mechanismanalyze whether<div><p>Many increasingly complex multi-objective optimization problems are emerging, and there is an urgent need to develop new multi-objective optimization algorithms to meet the challenges. This study introduces the Multi-Objective Dung Beetle Optimization Algorithm (MODBO), which integrates competitive and neighborhood mechanisms to tackle such problems, Thanks to the dung beetle optimization algorithm’s fast convergence and robust optimization finding ability in single-objective optimization algorithms. The introduction of non-dominated sorting allows the Dung Beetle Optimization Algorithm to solve multi-objective optimization problems (MOPs). To make the Dung Beetle Optimization Algorithm maintain good search ability in searching, we introduce a Competition mechanism to guide the particles’ global optimal search and a Neighborhood mechanism to guide the particles’ local optimal value search. An external archive is introduced to make each generation positionally optimal. Finally, to analyze whether the MODBO algorithm’s improved strategy is effective, a comparison with the nine algorithms on CEC2020 was made, and the 3D sensor deployment problem was used to demonstrate that the MODBO algorithm can solve realistic problems.</p></div>2025-10-14T17:25:35ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0331713.t005https://figshare.com/articles/dataset/Wilcoxon_test_for_MODBO_and_other_MO_competitors_/30357089CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303570892025-10-14T17:25:35Z |
| spellingShingle | Wilcoxon test for MODBO and other MO competitors. Wenxing Wu (16853002) Medicine Biotechnology Evolutionary Biology Ecology Developmental Biology Science Policy Computational Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified particles &# 8217 global optimal search generation positionally optimal dominated sorting allows objective optimization algorithms solving complex multi develop new multi objective optimization problems nine algorithms solve multi urgent need study introduces novel algorithm neighborhood mechanisms neighborhood mechanism mops ). modbo algorithm modbo ), integrates competitive improved strategy fast convergence external archive competition mechanism analyze whether |
| status_str | publishedVersion |
| title | Wilcoxon test for MODBO and other MO competitors. |
| title_full | Wilcoxon test for MODBO and other MO competitors. |
| title_fullStr | Wilcoxon test for MODBO and other MO competitors. |
| title_full_unstemmed | Wilcoxon test for MODBO and other MO competitors. |
| title_short | Wilcoxon test for MODBO and other MO competitors. |
| title_sort | Wilcoxon test for MODBO and other MO competitors. |
| topic | Medicine Biotechnology Evolutionary Biology Ecology Developmental Biology Science Policy Computational Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified particles &# 8217 global optimal search generation positionally optimal dominated sorting allows objective optimization algorithms solving complex multi develop new multi objective optimization problems nine algorithms solve multi urgent need study introduces novel algorithm neighborhood mechanisms neighborhood mechanism mops ). modbo algorithm modbo ), integrates competitive improved strategy fast convergence external archive competition mechanism analyze whether |