A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm
This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a fe...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870232/ |
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| author | Sanyi Li Wenjie Hou Peng Liu Weichao Yue Qian Wang |
| author_facet | Sanyi Li Wenjie Hou Peng Liu Weichao Yue Qian Wang |
| author_sort | Sanyi Li |
| collection | DOAJ |
| description | This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a feedforward neural network (FNN) along with special points to generate an initial population. The FNN is trained with historical special points (knee point, boundary point, center point), and the current special points are generated by the FNN when an environmental change is detected. Then the decision variables are classified into convergence variables and diversity variables. The convergence variables of special points are locally searched to form a new population and the best individuals of this population are selected. Finally, a portion of the initial population is generated by conducting a local search on the diversity variables of best individuals, while the remaining portion is produced using random strategies. SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. The proposed algorithm has been compared with other state-of-the-art algorithms on a series of benchmark problems, demonstrating its superior performance in optimizing DMOPs. |
| format | Article |
| id | doaj-art-9a04ab485635457c966d9cd854d3be02 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9a04ab485635457c966d9cd854d3be022025-02-12T00:02:07ZengIEEEIEEE Access2169-35362025-01-0113247652479210.1109/ACCESS.2025.353853710870232A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization AlgorithmSanyi Li0https://orcid.org/0009-0009-0373-7562Wenjie Hou1https://orcid.org/0009-0004-8054-2703Peng Liu2Weichao Yue3Qian Wang4https://orcid.org/0000-0001-9525-8124School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, ChinaThis paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a feedforward neural network (FNN) along with special points to generate an initial population. The FNN is trained with historical special points (knee point, boundary point, center point), and the current special points are generated by the FNN when an environmental change is detected. Then the decision variables are classified into convergence variables and diversity variables. The convergence variables of special points are locally searched to form a new population and the best individuals of this population are selected. Finally, a portion of the initial population is generated by conducting a local search on the diversity variables of best individuals, while the remaining portion is produced using random strategies. SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. The proposed algorithm has been compared with other state-of-the-art algorithms on a series of benchmark problems, demonstrating its superior performance in optimizing DMOPs.https://ieeexplore.ieee.org/document/10870232/Dynamic multi-objective optimizationdecision variable classificationirregular environmentneural networkpredictionspecial point |
| spellingShingle | Sanyi Li Wenjie Hou Peng Liu Weichao Yue Qian Wang A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm IEEE Access Dynamic multi-objective optimization decision variable classification irregular environment neural network prediction special point |
| title | A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm |
| title_full | A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm |
| title_fullStr | A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm |
| title_full_unstemmed | A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm |
| title_short | A Special Points and Neural Network-Based Dynamic Multi-Objective Optimization Algorithm |
| title_sort | special points and neural network based dynamic multi objective optimization algorithm |
| topic | Dynamic multi-objective optimization decision variable classification irregular environment neural network prediction special point |
| url | https://ieeexplore.ieee.org/document/10870232/ |
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