A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points
The Dynamic Multi-objective Optimization Problem (DMOP) is one of the common problem types in academia and industry. The Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) is an effective way for solving DMOPs. Despite the existence of many research works proposing a variety of DMOEAs, the deman...
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2025-06-01
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| author | Junxia Ma Yongxuan Sang Yaoli Xu Bo Wang |
| author_facet | Junxia Ma Yongxuan Sang Yaoli Xu Bo Wang |
| author_sort | Junxia Ma |
| collection | DOAJ |
| description | The Dynamic Multi-objective Optimization Problem (DMOP) is one of the common problem types in academia and industry. The Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) is an effective way for solving DMOPs. Despite the existence of many research works proposing a variety of DMOEAs, the demand for efficient solutions to DMOPs in drastically changing scenarios is still not well met. To this end, this paper is oriented towards DMOEA and innovatively proposes to explore the correlation between different points of the optimal frontier (PF) to improve the accuracy of predicting new PFs for new environments, which is the first attempt, to our best knowledge. Specifically, when the DMOP environment changes, this paper first constructs a spatio-temporal correlation model between various key points of the PF based on the linear regression algorithm; then, based on the constructed model, predicts a new location for each key point in the new environment; subsequently, constructs a sub-population by introducing the Gaussian noise into the predicted location to improve the generalization ability; and then, utilizes the idea of NSGA-II-B to construct another sub-population to further improve the population diversity; finally, combining the previous two sub-populations, re-initializing a new population to adapt to the new environment through a random replacement strategy. The proposed method was evaluated by experiments on the CEC 2018 test suite, and the experimental results show that the proposed method can obtain the optimal MIGD value on six DMOPs and the optimal MHVD value on five DMOPs, compared with six recent research results. |
| format | Article |
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| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-e0b6b281be164b18985b6b0dcbf12e772025-08-20T03:30:24ZengMDPI AGAlgorithms1999-48932025-06-0118637210.3390/a18060372A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front PointsJunxia Ma0Yongxuan Sang1Yaoli Xu2Bo Wang3College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, ChinaThe Dynamic Multi-objective Optimization Problem (DMOP) is one of the common problem types in academia and industry. The Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) is an effective way for solving DMOPs. Despite the existence of many research works proposing a variety of DMOEAs, the demand for efficient solutions to DMOPs in drastically changing scenarios is still not well met. To this end, this paper is oriented towards DMOEA and innovatively proposes to explore the correlation between different points of the optimal frontier (PF) to improve the accuracy of predicting new PFs for new environments, which is the first attempt, to our best knowledge. Specifically, when the DMOP environment changes, this paper first constructs a spatio-temporal correlation model between various key points of the PF based on the linear regression algorithm; then, based on the constructed model, predicts a new location for each key point in the new environment; subsequently, constructs a sub-population by introducing the Gaussian noise into the predicted location to improve the generalization ability; and then, utilizes the idea of NSGA-II-B to construct another sub-population to further improve the population diversity; finally, combining the previous two sub-populations, re-initializing a new population to adapt to the new environment through a random replacement strategy. The proposed method was evaluated by experiments on the CEC 2018 test suite, and the experimental results show that the proposed method can obtain the optimal MIGD value on six DMOPs and the optimal MHVD value on five DMOPs, compared with six recent research results.https://www.mdpi.com/1999-4893/18/6/372linear regressionDMOPDMOEAevolutionary algorithm |
| spellingShingle | Junxia Ma Yongxuan Sang Yaoli Xu Bo Wang A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points Algorithms linear regression DMOP DMOEA evolutionary algorithm |
| title | A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points |
| title_full | A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points |
| title_fullStr | A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points |
| title_full_unstemmed | A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points |
| title_short | A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points |
| title_sort | linear regression prediction based dynamic multi objective evolutionary algorithm with correlations of pareto front points |
| topic | linear regression DMOP DMOEA evolutionary algorithm |
| url | https://www.mdpi.com/1999-4893/18/6/372 |
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