Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization
The Slime Mould Algorithm (SMA) has gained significant attention from researchers due to its powerful multi-point search capability and its simple and practical structure. Several advanced versions of SMA have been proposed. However, most existing methods primarily focus on the single-objective rese...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10138407/ |
| _version_ | 1797774857824370688 |
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| author | Xuebing Cai Zengyu He Fei Cheng |
| author_facet | Xuebing Cai Zengyu He Fei Cheng |
| author_sort | Xuebing Cai |
| collection | DOAJ |
| description | The Slime Mould Algorithm (SMA) has gained significant attention from researchers due to its powerful multi-point search capability and its simple and practical structure. Several advanced versions of SMA have been proposed. However, most existing methods primarily focus on the single-objective research domain, leaving the research on multi-objective SMA relatively limited. Additionally, the basic SMA lacks robust global search capability, and extending it to the multi-objective domain often results in a loss of solution diversity. To address these limitations, this paper introduces a general multi-objective SMA framework. It incorporates a logical chaotic single-dimensional perturbation mechanism to enhance individuals’ search traversal in the decision space. Furthermore, a non-dominated sorting mechanism based on the reference point is employed to select a more diverse set of solutions for the subsequent evolution of the next generation. Through experiments conducted on 28 basis functions using seven advanced multi-objective algorithms (CMOPSO, NSGA-II, NSGA-III, MOEAD, PSEA-II, SPEA-II, and NSLS), the results demonstrate that the multi-objective SMA outperforms other algorithms in terms of convergence, accuracy, and diversity. |
| first_indexed | 2024-03-12T22:27:19Z |
| format | Article |
| id | doaj.art-123a1f7441984939ac9dafc909521789 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T22:27:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-123a1f7441984939ac9dafc9095217892023-07-21T23:00:30ZengIEEEIEEE Access2169-35362023-01-0111720887210010.1109/ACCESS.2023.328094310138407Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective OptimizationXuebing Cai0https://orcid.org/0009-0007-9849-2590Zengyu He1Fei Cheng2https://orcid.org/0000-0002-2245-9723Anhui Institute of Information Technology, Wuhu, ChinaAnhui Institute of Information Technology, Wuhu, ChinaManagement School, Hangzhou Dianzi University, Hangzhou, ChinaThe Slime Mould Algorithm (SMA) has gained significant attention from researchers due to its powerful multi-point search capability and its simple and practical structure. Several advanced versions of SMA have been proposed. However, most existing methods primarily focus on the single-objective research domain, leaving the research on multi-objective SMA relatively limited. Additionally, the basic SMA lacks robust global search capability, and extending it to the multi-objective domain often results in a loss of solution diversity. To address these limitations, this paper introduces a general multi-objective SMA framework. It incorporates a logical chaotic single-dimensional perturbation mechanism to enhance individuals’ search traversal in the decision space. Furthermore, a non-dominated sorting mechanism based on the reference point is employed to select a more diverse set of solutions for the subsequent evolution of the next generation. Through experiments conducted on 28 basis functions using seven advanced multi-objective algorithms (CMOPSO, NSGA-II, NSGA-III, MOEAD, PSEA-II, SPEA-II, and NSLS), the results demonstrate that the multi-objective SMA outperforms other algorithms in terms of convergence, accuracy, and diversity.https://ieeexplore.ieee.org/document/10138407/Slime mould algorithma logical chaotic single-dimensional perturbationreference pointmulti-objective |
| spellingShingle | Xuebing Cai Zengyu He Fei Cheng Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization IEEE Access Slime mould algorithm a logical chaotic single-dimensional perturbation reference point multi-objective |
| title | Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization |
| title_full | Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization |
| title_fullStr | Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization |
| title_full_unstemmed | Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization |
| title_short | Improved Slime Mould Algorithm Using Logical Chaos Perturbation and Reference Point Non-Dominated Sorting for Multi-Objective Optimization |
| title_sort | improved slime mould algorithm using logical chaos perturbation and reference point non dominated sorting for multi objective optimization |
| topic | Slime mould algorithm a logical chaotic single-dimensional perturbation reference point multi-objective |
| url | https://ieeexplore.ieee.org/document/10138407/ |
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