Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems
Abstract This paper proposes a novel algorithm named surrogate ensemble assisted differential evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale expensive optimization problems (LSEOPs) based on the CC framework. In the decomposition phase, our proposed EDDG...
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| Format: | Article |
| Language: | English |
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Springer
2023-10-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01262-6 |
| _version_ | 1797233188193435648 |
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| author | Rui Zhong Enzhi Zhang Masaharu Munetomo |
| author_facet | Rui Zhong Enzhi Zhang Masaharu Munetomo |
| author_sort | Rui Zhong |
| collection | DOAJ |
| description | Abstract This paper proposes a novel algorithm named surrogate ensemble assisted differential evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale expensive optimization problems (LSEOPs) based on the CC framework. In the decomposition phase, our proposed EDDG inherits the framework of efficient recursive differential grouping (ERDG) and embeds the multiplicative interaction identification technique of Dual DG (DDG), which can detect the additive and multiplicative interactions simultaneously without extra fitness evaluation consumption. Inspired by RDG2 and RDG3, we design the adaptive determination threshold and further decompose relatively large-scale sub-components to alleviate the curse of dimensionality. In the optimization phase, the SEADE is adopted as the basic optimizer, where the global and the local surrogate model are constructed by generalized regression neural network (GRNN) with all historical samples and Gaussian process regression (GPR) with recent samples. Expected improvement (EI) infill sampling criterion cooperated with random search is employed to search elite solutions in the surrogate model. To evaluate the performance of our proposal, we implement comprehensive experiments on CEC2013 benchmark functions compared with state-of-the-art decomposition techniques. Experimental and statistical results show that our proposed EDDG is competitive with these advanced decomposition techniques, and the introduction of SEADE can accelerate the convergence of optimization significantly. |
| first_indexed | 2024-04-24T16:12:12Z |
| format | Article |
| id | doaj.art-7d8ad70b5ff449eb9bc91df19b475d4f |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-24T16:12:12Z |
| publishDate | 2023-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-7d8ad70b5ff449eb9bc91df19b475d4f2024-03-31T11:39:52ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-10-011022129214910.1007/s40747-023-01262-6Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problemsRui Zhong0Enzhi Zhang1Masaharu Munetomo2Graduate School of Information Science and Technology, Hokkaido UniversityGraduate School of Information Science and Technology, Hokkaido UniversityInformation Initiative Center, Hokkaido UniversityAbstract This paper proposes a novel algorithm named surrogate ensemble assisted differential evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale expensive optimization problems (LSEOPs) based on the CC framework. In the decomposition phase, our proposed EDDG inherits the framework of efficient recursive differential grouping (ERDG) and embeds the multiplicative interaction identification technique of Dual DG (DDG), which can detect the additive and multiplicative interactions simultaneously without extra fitness evaluation consumption. Inspired by RDG2 and RDG3, we design the adaptive determination threshold and further decompose relatively large-scale sub-components to alleviate the curse of dimensionality. In the optimization phase, the SEADE is adopted as the basic optimizer, where the global and the local surrogate model are constructed by generalized regression neural network (GRNN) with all historical samples and Gaussian process regression (GPR) with recent samples. Expected improvement (EI) infill sampling criterion cooperated with random search is employed to search elite solutions in the surrogate model. To evaluate the performance of our proposal, we implement comprehensive experiments on CEC2013 benchmark functions compared with state-of-the-art decomposition techniques. Experimental and statistical results show that our proposed EDDG is competitive with these advanced decomposition techniques, and the introduction of SEADE can accelerate the convergence of optimization significantly.https://doi.org/10.1007/s40747-023-01262-6Cooperative coevolution (CC)Large-scale expensive optimization problems (LSEOPs)Efficient dual differential grouping (EDDG)Surrogate ensemble assisted differential evolution (SEADE) |
| spellingShingle | Rui Zhong Enzhi Zhang Masaharu Munetomo Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems Complex & Intelligent Systems Cooperative coevolution (CC) Large-scale expensive optimization problems (LSEOPs) Efficient dual differential grouping (EDDG) Surrogate ensemble assisted differential evolution (SEADE) |
| title | Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems |
| title_full | Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems |
| title_fullStr | Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems |
| title_full_unstemmed | Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems |
| title_short | Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems |
| title_sort | cooperative coevolutionary surrogate ensemble assisted differential evolution with efficient dual differential grouping for large scale expensive optimization problems |
| topic | Cooperative coevolution (CC) Large-scale expensive optimization problems (LSEOPs) Efficient dual differential grouping (EDDG) Surrogate ensemble assisted differential evolution (SEADE) |
| url | https://doi.org/10.1007/s40747-023-01262-6 |
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