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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.17889 |
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| _version_ | 1866916336061382656 |
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| author | Zhao, Qing Zhang, Chengkui Li, Hao Ke, Ting |
| author_facet | Zhao, Qing Zhang, Chengkui Li, Hao Ke, Ting |
| contents | BPSO algorithm is a swarm intelligence optimization algorithm, which has the characteristics of good optimization effect, high efficiency and easy to implement. In recent years, it has been used to optimize a variety of machine learning and deep learning models, such as CNN, LSTM, SVM, etc. But it is easy to fall into local optimum for the lack of exploitation ability. It is found that in the article, which is different from previous studies, The reason for the poor performance is an error existing in their velocity update function, which leads to abnormal and chaotic behavior of particles. This not only makes the algorithm difficult to converge, but also often searches the repeated space. So, traditionally, it has to rely on a low w value in the later stage to force these algorithms to converge, but also makes them quickly lose their search ability and prone to getting trapped in local optima. This article proposes a velocity legacy term correction method for all V-shaped BPSOs. Experimentals based on 0/1 knapsack problems show that it has a significant effect on accuracy and efficiency for all of the 4 commonly used V-Shaped BPSOs. Therefore it is an significant breakthrough in the field of swarm intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17889 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Error Discovery and Correction for the Family of V-Shaped BPSO Algorithms Zhao, Qing Zhang, Chengkui Li, Hao Ke, Ting Neural and Evolutionary Computing BPSO algorithm is a swarm intelligence optimization algorithm, which has the characteristics of good optimization effect, high efficiency and easy to implement. In recent years, it has been used to optimize a variety of machine learning and deep learning models, such as CNN, LSTM, SVM, etc. But it is easy to fall into local optimum for the lack of exploitation ability. It is found that in the article, which is different from previous studies, The reason for the poor performance is an error existing in their velocity update function, which leads to abnormal and chaotic behavior of particles. This not only makes the algorithm difficult to converge, but also often searches the repeated space. So, traditionally, it has to rely on a low w value in the later stage to force these algorithms to converge, but also makes them quickly lose their search ability and prone to getting trapped in local optima. This article proposes a velocity legacy term correction method for all V-shaped BPSOs. Experimentals based on 0/1 knapsack problems show that it has a significant effect on accuracy and efficiency for all of the 4 commonly used V-Shaped BPSOs. Therefore it is an significant breakthrough in the field of swarm intelligence. |
| title | An Error Discovery and Correction for the Family of V-Shaped BPSO Algorithms |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2407.17889 |