An Efficient Multi-Objective White Shark Algorithm
To balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a multi-objective White Shark Optimization algorithm (MONSWSO) tailored for multi-objective optimization. MONSWSO integrates non-dominated sorting and crowding distance into the White S...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/2/112 |
| _version_ | 1826582971105148928 |
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| author | Wenyan Guo Yufan Qiang Fang Dai Junfeng Wang Shenglong Li |
| author_facet | Wenyan Guo Yufan Qiang Fang Dai Junfeng Wang Shenglong Li |
| author_sort | Wenyan Guo |
| collection | DOAJ |
| description | To balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a multi-objective White Shark Optimization algorithm (MONSWSO) tailored for multi-objective optimization. MONSWSO integrates non-dominated sorting and crowding distance into the White Shark Optimization framework to select the optimal solution within the population. The uniformity of the initial population is enhanced through a chaotic reverse initialization learning strategy. The adaptive updating of individual positions is facilitated by an elite-guided forgetting mechanism, which incorporates escape energy and eddy aggregation behavior inspired by marine organisms to improve exploration in key areas. To evaluate the effectiveness of MONSWSO, it is benchmarked against five state-of-the-art multi-objective algorithms using four metrics: inverse generation distance, spatial homogeneity, spatial distribution, and hypervolume on 27 typical problems, including 23 multi-objective functions and 4 multi-objective project examples. Furthermore, the practical application of MONSWSO is demonstrated through an example of optimizing the design of subway tunnel foundation pits. The comprehensive results reveal that MONSWSO outperforms the comparison algorithms, achieving impressive and satisfactory outcomes. |
| first_indexed | 2025-03-14T15:14:26Z |
| format | Article |
| id | doaj.art-22cc52d0fd0748ea8e8dab0d0f8cfb2c |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2025-03-14T15:14:26Z |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-22cc52d0fd0748ea8e8dab0d0f8cfb2c2025-02-25T13:15:51ZengMDPI AGBiomimetics2313-76732025-02-0110211210.3390/biomimetics10020112An Efficient Multi-Objective White Shark AlgorithmWenyan Guo0Yufan Qiang1Fang Dai2Junfeng Wang3Shenglong Li4School of Science, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Science, Xi’an University of Technology, Xi’an 710048, ChinaTo balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a multi-objective White Shark Optimization algorithm (MONSWSO) tailored for multi-objective optimization. MONSWSO integrates non-dominated sorting and crowding distance into the White Shark Optimization framework to select the optimal solution within the population. The uniformity of the initial population is enhanced through a chaotic reverse initialization learning strategy. The adaptive updating of individual positions is facilitated by an elite-guided forgetting mechanism, which incorporates escape energy and eddy aggregation behavior inspired by marine organisms to improve exploration in key areas. To evaluate the effectiveness of MONSWSO, it is benchmarked against five state-of-the-art multi-objective algorithms using four metrics: inverse generation distance, spatial homogeneity, spatial distribution, and hypervolume on 27 typical problems, including 23 multi-objective functions and 4 multi-objective project examples. Furthermore, the practical application of MONSWSO is demonstrated through an example of optimizing the design of subway tunnel foundation pits. The comprehensive results reveal that MONSWSO outperforms the comparison algorithms, achieving impressive and satisfactory outcomes.https://www.mdpi.com/2313-7673/10/2/112White Shark Optimization algorithmmulti-objective optimizationnon-dominated sortingelite reservationsubway tunnel foundation pits optimization |
| spellingShingle | Wenyan Guo Yufan Qiang Fang Dai Junfeng Wang Shenglong Li An Efficient Multi-Objective White Shark Algorithm Biomimetics White Shark Optimization algorithm multi-objective optimization non-dominated sorting elite reservation subway tunnel foundation pits optimization |
| title | An Efficient Multi-Objective White Shark Algorithm |
| title_full | An Efficient Multi-Objective White Shark Algorithm |
| title_fullStr | An Efficient Multi-Objective White Shark Algorithm |
| title_full_unstemmed | An Efficient Multi-Objective White Shark Algorithm |
| title_short | An Efficient Multi-Objective White Shark Algorithm |
| title_sort | efficient multi objective white shark algorithm |
| topic | White Shark Optimization algorithm multi-objective optimization non-dominated sorting elite reservation subway tunnel foundation pits optimization |
| url | https://www.mdpi.com/2313-7673/10/2/112 |
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