A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework
Abstract Weighted optimization framework (WOF) achieves variable dimensionality reduction by grouping variables and optimizing weights, playing an important role in large-scale multi-objective optimization problems. However, because of possible problems such as duplicate weight vectors in the select...
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| Format: | Article |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-64570-y |
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| author | Lixue Xiong Debao Chen Feng Zou Fangzhen Ge Fuqiang Liu |
| author_facet | Lixue Xiong Debao Chen Feng Zou Fangzhen Ge Fuqiang Liu |
| author_sort | Lixue Xiong |
| collection | DOAJ |
| description | Abstract Weighted optimization framework (WOF) achieves variable dimensionality reduction by grouping variables and optimizing weights, playing an important role in large-scale multi-objective optimization problems. However, because of possible problems such as duplicate weight vectors in the selection process and loss of population diversity, the algorithm is susceptible to local optimization. Therefore, this paper develops an algorithm framework called multi-population multi-stage adaptive weighted optimization (MPSOF) to improve the performance of WOF in two aspects. First, the method of using multi-population is employed to address the issue of insufficient algorithmic diversity, while simultaneously reducing the likelihood of converging towards local optima. Secondly, a processing stage is incorporated into MPSOF, where a certain number of individuals are adaptively selected for updating based on the weight information and evolutionary status of different subpopulations, targeting different types of weights. This approach alleviates the impact of repetitive weights on the diversity of newly generated individuals, avoids the drawback of easily converging to local optima when using a single type of weight for updating, and effectively balances the diversity and convergence of subpopulations. Experiments of three types designed on several typical function sets demonstrate that MPSOF exceeds the comparison algorithms in the three metrics for Inverse Generation Distance, Hypervolume and Spacing. |
| format | Article |
| id | doaj-art-34dd162522994918899f44a6423172aa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-34dd162522994918899f44a6423172aa2025-01-26T12:34:43ZengNature PortfolioScientific Reports2045-23222024-06-0114113110.1038/s41598-024-64570-yA multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm frameworkLixue Xiong0Debao Chen1Feng Zou2Fangzhen Ge3Fuqiang Liu4School of Physics and Electronic Information, Huaibei Normal UniversitySchool of Physics and Electronic Information, Huaibei Normal UniversitySchool of Physics and Electronic Information, Huaibei Normal UniversitySchool of Computer Science and Technology, Huaibei Normal UniversitySchool of Computer Science and Technology, Huaibei Normal UniversityAbstract Weighted optimization framework (WOF) achieves variable dimensionality reduction by grouping variables and optimizing weights, playing an important role in large-scale multi-objective optimization problems. However, because of possible problems such as duplicate weight vectors in the selection process and loss of population diversity, the algorithm is susceptible to local optimization. Therefore, this paper develops an algorithm framework called multi-population multi-stage adaptive weighted optimization (MPSOF) to improve the performance of WOF in two aspects. First, the method of using multi-population is employed to address the issue of insufficient algorithmic diversity, while simultaneously reducing the likelihood of converging towards local optima. Secondly, a processing stage is incorporated into MPSOF, where a certain number of individuals are adaptively selected for updating based on the weight information and evolutionary status of different subpopulations, targeting different types of weights. This approach alleviates the impact of repetitive weights on the diversity of newly generated individuals, avoids the drawback of easily converging to local optima when using a single type of weight for updating, and effectively balances the diversity and convergence of subpopulations. Experiments of three types designed on several typical function sets demonstrate that MPSOF exceeds the comparison algorithms in the three metrics for Inverse Generation Distance, Hypervolume and Spacing.https://doi.org/10.1038/s41598-024-64570-y |
| spellingShingle | Lixue Xiong Debao Chen Feng Zou Fangzhen Ge Fuqiang Liu A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework Scientific Reports |
| title | A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework |
| title_full | A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework |
| title_fullStr | A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework |
| title_full_unstemmed | A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework |
| title_short | A multi-population multi-stage adaptive weighted large-scale multi-objective optimization algorithm framework |
| title_sort | multi population multi stage adaptive weighted large scale multi objective optimization algorithm framework |
| url | https://doi.org/10.1038/s41598-024-64570-y |
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