Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm
In order to solve the problem of optimal control of the sewage treatment process based on a multiobjective evolutionary algorithm, an intelligent optimal control of sewage treatment based on a multiobjective evolutionary algorithm is proposed in this paper. In this paper, the decomposition based mul...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/6218545 |
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| author | Xi’ning Jia Chengmi Xiang Jin Wang Xue Gao Yunrui Ye |
| author_facet | Xi’ning Jia Chengmi Xiang Jin Wang Xue Gao Yunrui Ye |
| author_sort | Xi’ning Jia |
| collection | DOAJ |
| description | In order to solve the problem of optimal control of the sewage treatment process based on a multiobjective evolutionary algorithm, an intelligent optimal control of sewage treatment based on a multiobjective evolutionary algorithm is proposed in this paper. In this paper, the decomposition based multiobjective evolutionary algorithm (MOEA/D) is improved, and it is expected that the uniformly distributed approximate Pareto frontier can be obtained with fewer evolution times. For each new solution generated by the MOEA/D algorithm, the improved algorithm in this paper finds the most suitable subproblem for the new solution from all subproblems and replaces the population in its neighborhood. On the basis of the original subproblem, it carries out secondary optimization to improve the utilization rate of the children and then finds the approximate Pareto frontier in the optimization problem with fewer iterations. The experimental results show that AE, PE, and EC Based on SS–MOEA/D optimal control method are reduced by 6.91%, 1.54%, and 5.58%, respectively. Conclusion. The algorithm significantly reduces the number of steps to find the Pareto frontier, significantly improves the performance of the MOEA/D algorithm, and achieves the optimization goal in the optimization of the sewage treatment process. |
| format | Article |
| id | doaj-art-66888cc714e541dc8b22bc3e02c312f0 |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-66888cc714e541dc8b22bc3e02c312f02025-08-20T03:34:45ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/6218545Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary AlgorithmXi’ning Jia0Chengmi Xiang1Jin Wang2Xue Gao3Yunrui Ye4Xi’an Kedagaoxin UniversityXi’an Kedagaoxin UniversityXi’an Kedagaoxin UniversityXi’an University of Posts and TelecommunicationsXi’an University of Posts and TelecommunicationsIn order to solve the problem of optimal control of the sewage treatment process based on a multiobjective evolutionary algorithm, an intelligent optimal control of sewage treatment based on a multiobjective evolutionary algorithm is proposed in this paper. In this paper, the decomposition based multiobjective evolutionary algorithm (MOEA/D) is improved, and it is expected that the uniformly distributed approximate Pareto frontier can be obtained with fewer evolution times. For each new solution generated by the MOEA/D algorithm, the improved algorithm in this paper finds the most suitable subproblem for the new solution from all subproblems and replaces the population in its neighborhood. On the basis of the original subproblem, it carries out secondary optimization to improve the utilization rate of the children and then finds the approximate Pareto frontier in the optimization problem with fewer iterations. The experimental results show that AE, PE, and EC Based on SS–MOEA/D optimal control method are reduced by 6.91%, 1.54%, and 5.58%, respectively. Conclusion. The algorithm significantly reduces the number of steps to find the Pareto frontier, significantly improves the performance of the MOEA/D algorithm, and achieves the optimization goal in the optimization of the sewage treatment process.http://dx.doi.org/10.1155/2022/6218545 |
| spellingShingle | Xi’ning Jia Chengmi Xiang Jin Wang Xue Gao Yunrui Ye Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm Journal of Control Science and Engineering |
| title | Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm |
| title_full | Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm |
| title_fullStr | Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm |
| title_full_unstemmed | Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm |
| title_short | Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm |
| title_sort | intelligent optimal control of sewage treatment based on multiobjective evolutionary algorithm |
| url | http://dx.doi.org/10.1155/2022/6218545 |
| work_keys_str_mv | AT xiningjia intelligentoptimalcontrolofsewagetreatmentbasedonmultiobjectiveevolutionaryalgorithm AT chengmixiang intelligentoptimalcontrolofsewagetreatmentbasedonmultiobjectiveevolutionaryalgorithm AT jinwang intelligentoptimalcontrolofsewagetreatmentbasedonmultiobjectiveevolutionaryalgorithm AT xuegao intelligentoptimalcontrolofsewagetreatmentbasedonmultiobjectiveevolutionaryalgorithm AT yunruiye intelligentoptimalcontrolofsewagetreatmentbasedonmultiobjectiveevolutionaryalgorithm |