A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection
Abstract Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during the evolutionary process. This paper proposes a novel...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-07-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00812-8 |
| _version_ | 1797863237734105088 |
|---|---|
| author | Huantong Geng Ke Xu Yanqi Zhang Zhengli Zhou |
| author_facet | Huantong Geng Ke Xu Yanqi Zhang Zhengli Zhou |
| author_sort | Huantong Geng |
| collection | DOAJ |
| description | Abstract Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during the evolutionary process. This paper proposes a novel classification tree based adaptive operator selection strategy for multi-objective evolutionary algorithm based on decomposition (MOEA/D-CTAOS). In our proposal, the classification tree is trained by the recorded data set which contains the information on the historical offspring. Before the reproduction at each generation, the classifier is used to predict each possible result obtained by different operators, and only one operator with the best result is selected to generate offspring next. Meanwhile, a novel differential evolution based on search inertia (SiDE) is designed to steer the evolutionary process in a more efficient way. The experimental results demonstrate that proposed MOEA/D-CTAOS outperforms other MOEA/D variants on UF and LZ benchmarks in terms of IGD and HV value. Further investigation also confirms the advantage of direction-guided search strategy in SiDE. |
| first_indexed | 2024-04-09T22:32:21Z |
| format | Article |
| id | doaj.art-fbca89ab68bd40799d091dfebad02644 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-09T22:32:21Z |
| publishDate | 2022-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-fbca89ab68bd40799d091dfebad026442023-03-22T12:43:47ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-07-019157959610.1007/s40747-022-00812-8A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selectionHuantong Geng0Ke Xu1Yanqi Zhang2Zhengli Zhou3Nanjing University of Information Science and TechnologyNanjing University of Information Science and TechnologyNanjing University of Information Science and TechnologyNanjing University of Information Science and TechnologyAbstract Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during the evolutionary process. This paper proposes a novel classification tree based adaptive operator selection strategy for multi-objective evolutionary algorithm based on decomposition (MOEA/D-CTAOS). In our proposal, the classification tree is trained by the recorded data set which contains the information on the historical offspring. Before the reproduction at each generation, the classifier is used to predict each possible result obtained by different operators, and only one operator with the best result is selected to generate offspring next. Meanwhile, a novel differential evolution based on search inertia (SiDE) is designed to steer the evolutionary process in a more efficient way. The experimental results demonstrate that proposed MOEA/D-CTAOS outperforms other MOEA/D variants on UF and LZ benchmarks in terms of IGD and HV value. Further investigation also confirms the advantage of direction-guided search strategy in SiDE.https://doi.org/10.1007/s40747-022-00812-8Multi-objective optimizationAdaptive operator selectionClassification treeSearch inertia |
| spellingShingle | Huantong Geng Ke Xu Yanqi Zhang Zhengli Zhou A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection Complex & Intelligent Systems Multi-objective optimization Adaptive operator selection Classification tree Search inertia |
| title | A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection |
| title_full | A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection |
| title_fullStr | A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection |
| title_full_unstemmed | A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection |
| title_short | A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection |
| title_sort | classification tree and decomposition based multi objective evolutionary algorithm with adaptive operator selection |
| topic | Multi-objective optimization Adaptive operator selection Classification tree Search inertia |
| url | https://doi.org/10.1007/s40747-022-00812-8 |
| work_keys_str_mv | AT huantonggeng aclassificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection AT kexu aclassificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection AT yanqizhang aclassificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection AT zhenglizhou aclassificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection AT huantonggeng classificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection AT kexu classificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection AT yanqizhang classificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection AT zhenglizhou classificationtreeanddecompositionbasedmultiobjectiveevolutionaryalgorithmwithadaptiveoperatorselection |