Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction
Abstract Extreme learning machine (ELM) is popular as a method of training single hidden layer feedforward neural networks. However, the ELMs optimized by the traditional gradient descent algorithms cannot fundamentally solve the influence of the random selection of the input weights and biases. The...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-11-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-022-00160-y |
| _version_ | 1798016742443712512 |
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| author | Xiangmin Zhang Yongquan Zhou Huajuan Huang Qifang Luo |
| author_facet | Xiangmin Zhang Yongquan Zhou Huajuan Huang Qifang Luo |
| author_sort | Xiangmin Zhang |
| collection | DOAJ |
| description | Abstract Extreme learning machine (ELM) is popular as a method of training single hidden layer feedforward neural networks. However, the ELMs optimized by the traditional gradient descent algorithms cannot fundamentally solve the influence of the random selection of the input weights and biases. Therefore, this paper proposes a method of extreme learning machine optimized by an enhanced salp search algorithm (NSSA-ELM). Salp search algorithm (SSA) is a metaheuristic algorithm, to improve the performance of SSA exploration and avoid getting stuck in local optima, the neighborhood centroid opposite‑based learning is used to optimize SSA. This method maintains the diversity of the population, which is conducive to avoid local optimization and accelerate convergence. This paper performs classification tests on NSSA and other metaheuristic-optimized ELMs on ten datasets, and regression tests on 5 datasets. Finally, the prediction ability of dew point temperature is evaluated. The meteorological data of five climatically representative cities in China from 2016 to 2022 were collected to predict the dew point temperature. The experimental results show that the NSSA-ELM is the best model, and its generalization performance and accuracy are better than other models. |
| first_indexed | 2024-04-11T15:55:38Z |
| format | Article |
| id | doaj.art-586a64f2ee0642839fcc0cb2f2bed5dc |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-11T15:55:38Z |
| publishDate | 2022-11-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-586a64f2ee0642839fcc0cb2f2bed5dc2022-12-22T04:15:10ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832022-11-0115112010.1007/s44196-022-00160-yEnhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature PredictionXiangmin Zhang0Yongquan Zhou1Huajuan Huang2Qifang Luo3College of Artificial Intelligence, Guangxi University for NationalitiesCollege of Artificial Intelligence, Guangxi University for NationalitiesCollege of Artificial Intelligence, Guangxi University for NationalitiesCollege of Artificial Intelligence, Guangxi University for NationalitiesAbstract Extreme learning machine (ELM) is popular as a method of training single hidden layer feedforward neural networks. However, the ELMs optimized by the traditional gradient descent algorithms cannot fundamentally solve the influence of the random selection of the input weights and biases. Therefore, this paper proposes a method of extreme learning machine optimized by an enhanced salp search algorithm (NSSA-ELM). Salp search algorithm (SSA) is a metaheuristic algorithm, to improve the performance of SSA exploration and avoid getting stuck in local optima, the neighborhood centroid opposite‑based learning is used to optimize SSA. This method maintains the diversity of the population, which is conducive to avoid local optimization and accelerate convergence. This paper performs classification tests on NSSA and other metaheuristic-optimized ELMs on ten datasets, and regression tests on 5 datasets. Finally, the prediction ability of dew point temperature is evaluated. The meteorological data of five climatically representative cities in China from 2016 to 2022 were collected to predict the dew point temperature. The experimental results show that the NSSA-ELM is the best model, and its generalization performance and accuracy are better than other models.https://doi.org/10.1007/s44196-022-00160-yExtreme learning machineSalp search algorithmNSSA-ELMDew point temperatureMetaheuristic |
| spellingShingle | Xiangmin Zhang Yongquan Zhou Huajuan Huang Qifang Luo Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction International Journal of Computational Intelligence Systems Extreme learning machine Salp search algorithm NSSA-ELM Dew point temperature Metaheuristic |
| title | Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction |
| title_full | Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction |
| title_fullStr | Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction |
| title_full_unstemmed | Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction |
| title_short | Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction |
| title_sort | enhanced salp search algorithm for optimization extreme learning machine and application to dew point temperature prediction |
| topic | Extreme learning machine Salp search algorithm NSSA-ELM Dew point temperature Metaheuristic |
| url | https://doi.org/10.1007/s44196-022-00160-y |
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