A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination
The decomposition-based multi-objective optimization algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) introduces the concept of neighborhood, where each sub-problem requires optimization through solutions within its neighborhood. Due to the comparison being only with...
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MDPI AG
2025-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/1/19 |
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| author | Hongfeng Ma Jiaxu Ning Jie Zheng Changsheng Zhang |
| author_facet | Hongfeng Ma Jiaxu Ning Jie Zheng Changsheng Zhang |
| author_sort | Hongfeng Ma |
| collection | DOAJ |
| description | The decomposition-based multi-objective optimization algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) introduces the concept of neighborhood, where each sub-problem requires optimization through solutions within its neighborhood. Due to the comparison being only with solutions in the neighborhood, the obtained set of solutions is not sufficiently diverse, leading to poorer convergence properties. In order to adequately acquire a high-quality set of solutions, this algorithm requires a large number of population iterations, which in turn results in relatively low computational efficiency. To address this issue, this paper proposes an algorithm termed MOEA/D-NRD, which is based on neighborhood region domination in the MOEA/D framework. In the improved algorithm, domination relationships are determined by comparing offspring solutions against neighborhood ideal points and neighborhood worst points. By selecting appropriate solution sets within these comparison regions, the solution sets can approach the ideal points more and faster, thereby accelerating population convergence and enhancing the computational efficiency of the algorithm. |
| format | Article |
| id | doaj-art-1e73e6f21e8146b2ae21bcc077cca7c6 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-1e73e6f21e8146b2ae21bcc077cca7c62025-01-24T13:24:37ZengMDPI AGBiomimetics2313-76732025-01-011011910.3390/biomimetics10010019A Decomposition-Based Evolutionary Algorithm with Neighborhood Region DominationHongfeng Ma0Jiaxu Ning1Jie Zheng2Changsheng Zhang3School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSoftware College of Northeastern University, Northeastern University, Shenyang 110819, ChinaThe decomposition-based multi-objective optimization algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) introduces the concept of neighborhood, where each sub-problem requires optimization through solutions within its neighborhood. Due to the comparison being only with solutions in the neighborhood, the obtained set of solutions is not sufficiently diverse, leading to poorer convergence properties. In order to adequately acquire a high-quality set of solutions, this algorithm requires a large number of population iterations, which in turn results in relatively low computational efficiency. To address this issue, this paper proposes an algorithm termed MOEA/D-NRD, which is based on neighborhood region domination in the MOEA/D framework. In the improved algorithm, domination relationships are determined by comparing offspring solutions against neighborhood ideal points and neighborhood worst points. By selecting appropriate solution sets within these comparison regions, the solution sets can approach the ideal points more and faster, thereby accelerating population convergence and enhancing the computational efficiency of the algorithm.https://www.mdpi.com/2313-7673/10/1/19MOEA/Dneighborhoodneighborhood region dominationideal pointintelligence techniques |
| spellingShingle | Hongfeng Ma Jiaxu Ning Jie Zheng Changsheng Zhang A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination Biomimetics MOEA/D neighborhood neighborhood region domination ideal point intelligence techniques |
| title | A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination |
| title_full | A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination |
| title_fullStr | A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination |
| title_full_unstemmed | A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination |
| title_short | A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination |
| title_sort | decomposition based evolutionary algorithm with neighborhood region domination |
| topic | MOEA/D neighborhood neighborhood region domination ideal point intelligence techniques |
| url | https://www.mdpi.com/2313-7673/10/1/19 |
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