A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble
Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/11/2505 |
| _version_ | 1797597668974788608 |
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| author | Zhendong Wang Jianlan Wang Dahai Li Donglin Zhu |
| author_facet | Zhendong Wang Jianlan Wang Dahai Li Donglin Zhu |
| author_sort | Zhendong Wang |
| collection | DOAJ |
| description | Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows’ locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost. |
| first_indexed | 2024-03-11T03:08:49Z |
| format | Article |
| id | doaj.art-a5f3321dd15e44a6adf7e30876988b68 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:08:49Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a5f3321dd15e44a6adf7e30876988b682023-11-18T07:45:50ZengMDPI AGElectronics2079-92922023-06-011211250510.3390/electronics12112505A Multi-Strategy Sparrow Search Algorithm with Selective EnsembleZhendong Wang0Jianlan Wang1Dahai Li2Donglin Zhu3School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaCollege of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, ChinaAiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows’ locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost.https://www.mdpi.com/2079-9292/12/11/2505optimization problemssparrow search algorithmmulti-strategyselective ensemblepriority roulette selection |
| spellingShingle | Zhendong Wang Jianlan Wang Dahai Li Donglin Zhu A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble Electronics optimization problems sparrow search algorithm multi-strategy selective ensemble priority roulette selection |
| title | A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble |
| title_full | A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble |
| title_fullStr | A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble |
| title_full_unstemmed | A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble |
| title_short | A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble |
| title_sort | multi strategy sparrow search algorithm with selective ensemble |
| topic | optimization problems sparrow search algorithm multi-strategy selective ensemble priority roulette selection |
| url | https://www.mdpi.com/2079-9292/12/11/2505 |
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