A Centroid Guided Cluster Transformation for Dynamic Multi-Objective Optimization Algorithm
In recent years, prediction-based algorithms have made significant progress in solving dynamic multi-objective optimization problems (DMOPs). However, most existing algorithms only consider information from several consecutive environments and ignore previous search experiences. This article uses th...
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| Vydané v: | 2025 IEEE Congress on Evolutionary Computation (CEC) s. 1 - 8 |
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08.06.2025
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| Abstract | In recent years, prediction-based algorithms have made significant progress in solving dynamic multi-objective optimization problems (DMOPs). However, most existing algorithms only consider information from several consecutive environments and ignore previous search experiences. This article uses the search experience of the Regularity Model-Based Multiobjective Estimation of Distribution Algorithm (RM-MEDA) to propose a dynamic multiobjective optimization algorithm based on the centroid-guided cluster transformation (CGCT-RM- MEDA). When the environment changes, the information from the statically optimized model in the previous environment is used to estimate a new distribution model through a cluster transformation. At the same time, the location of the population distribution is predicted using a Long Short-Term Memory (LSTM) network, which is used to estimate the cluster centers of the model distribution. The empirical study evaluated the performance of CGCT-RM-MEDA using 14 benchmark functions and one performance metric. The experimental results show that CGCT-RM-MEDA outperforms seven peer algorithms in performance. |
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| AbstractList | In recent years, prediction-based algorithms have made significant progress in solving dynamic multi-objective optimization problems (DMOPs). However, most existing algorithms only consider information from several consecutive environments and ignore previous search experiences. This article uses the search experience of the Regularity Model-Based Multiobjective Estimation of Distribution Algorithm (RM-MEDA) to propose a dynamic multiobjective optimization algorithm based on the centroid-guided cluster transformation (CGCT-RM- MEDA). When the environment changes, the information from the statically optimized model in the previous environment is used to estimate a new distribution model through a cluster transformation. At the same time, the location of the population distribution is predicted using a Long Short-Term Memory (LSTM) network, which is used to estimate the cluster centers of the model distribution. The empirical study evaluated the performance of CGCT-RM-MEDA using 14 benchmark functions and one performance metric. The experimental results show that CGCT-RM-MEDA outperforms seven peer algorithms in performance. |
| Author | Lin, Fenglin Liu, Meitong Zeng, Yi Xia, Xuewen Zhang, Yuehui |
| Author_xml | – sequence: 1 givenname: Yi surname: Zeng fullname: Zeng, Yi email: yizengl020@126.com organization: Minnan Normal University,College of Physics and Information Engineering,Zhangzhou,China – sequence: 2 givenname: Xuewen surname: Xia fullname: Xia, Xuewen email: xwxia@whu.edu.cn organization: Minnan Normal University,College of Physics and Information Engineering,Zhangzhou,China – sequence: 3 givenname: Fenglin surname: Lin fullname: Lin, Fenglin email: 893699887@qq.com organization: Minnan Normal University,College of Physics and Information Engineering,Zhangzhou,China – sequence: 4 givenname: Yuehui surname: Zhang fullname: Zhang, Yuehui email: 1147113792@qq.com organization: Minnan Normal University,College of Physics and Information Engineering,Zhangzhou,China – sequence: 5 givenname: Meitong surname: Liu fullname: Liu, Meitong email: 2062408325@qq.com organization: Minnan Normal University,College of Physics and Information Engineering,Zhangzhou,China |
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| Snippet | In recent years, prediction-based algorithms have made significant progress in solving dynamic multi-objective optimization problems (DMOPs). However, most... |
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| SubjectTerms | Benchmark testing cluster transformation Clustering algorithms Dynamic multi-objective optimization algorithm Heuristic algorithms Long short term memory Long Short-Term Memory (LSTM) Optimization Prediction algorithms Predictive models Reliability |
| Title | A Centroid Guided Cluster Transformation for Dynamic Multi-Objective Optimization Algorithm |
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