Improved Sparrow Search Algorithm with the Extreme Learning Machine and Its Application for Prediction

Li, Jingjing; Wu, Yonghong

doi:10.1007/s11063-022-10804-x

Cited by 12 publications

(11 citation statements)

References 30 publications

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“…In this formula, S is the step size, and ( ) LS is the probability of moving the step size. Then, the Mantegna method is used to generate the random step size of Levy distribution [12]:…”

Section: Self-adaptive Levy Flight Strategy Improves Dung Beetle Stea...mentioning

confidence: 99%

Time difference of arrival location method based on improved dung beetle optimizer algorithm

Liu,

Nie,

Wang

et al. 2024

Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023)

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“…In this formula, S is the step size, and ( ) LS is the probability of moving the step size. Then, the Mantegna method is used to generate the random step size of Levy distribution [12]:…”

Section: Self-adaptive Levy Flight Strategy Improves Dung Beetle Stea...mentioning

confidence: 99%

Time difference of arrival location method based on improved dung beetle optimizer algorithm

Liu,

Nie,

Wang

et al. 2024

Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023)

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“…To avoid accidental errors, IPOA-ELM, POA-ELM, and ELM, three models are run on the data set. After 40 results, the mean and the maximum and minimum values are taken, and compared with the ISSA-ELM model in [32].…”

Section: Ipoa-elm Engineering Applicationsmentioning

confidence: 99%

Improved extreme learning machine based on adaptive dual-strategy optimization algorithm and its application

Yuan

Liang

Jiawei

et al. 2022

Preprint

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Aiming at the problem of low accuracy and unstable prediction results caused by the random connection weight threshold between the input layer and the hidden layer of extreme learning machine (ELM). An adaptive dual-strategy improved pelican optimization algorithm (IPOA) -ELM regression prediction model is proposed. Firstly, the pelican optimization algorithm (POA) is improved by the Logistic-Tent chaotic map, improved convergence factor by adaptive double strategy method and reverse learning strategy. Then, the performance of IPOA is verified by multiple groups of multi-dimensional single-peak and multi-peak test functions. The test results show that IPOA has better accuracy, stability, and robustness than POA, butterfly optimization algorithm (BOA), cuckoo algorithm (CS), grey wolf algorithm (GWO), particle swarm optimization algorithm (PSO), genetic algorithm (GA) and mouse swarm optimization algorithm (RSO). Finally, IPOA is applied to optimize ELM, and the performance of the IPOA-ELM model is verified by three engineering data sets. The simulation results show that the convergence accuracy, stability, and robustness of the IPOA-ELM model are better than those of the POA-ELM and ELM models.

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“…To address these issues, a single Hidden Layer (HL) feedforward neural network model called Extreme Learning Machine (ELM) has been proposed, which has significant advantages in parameter setting [7][8]. The operational efficiency refers to the resource utilization and execution efficiency exhibited by the model during task execution.ELM uses random initialization parameters to obtain output layer weights analytically, eliminating the need for parameter updates and greatly reducing the model's running time, making it an efficient neural network model [9]. However, ELM still faces challenges in addressing classification issues, such as model uncertainty due to random parameter generation and the inability to guarantee optimal classification performance.…”

Section: Introductionmentioning

confidence: 99%

“…The weight W and bias B from the input layer to the first HL are randomly initialized. The output matrix of the first HL is shown in formula(9).…”

mentioning

confidence: 99%

Optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

Zhang,

Wang

2024

IEEE Access

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Classification algorithms are one of the important research topics in the artificial intelligence, widely applied in various scientific and engineering fields. Extreme learning machine is a single hidden layer feed-forward neural network algorithm. Compared with traditional neural network models, the training speed and the generalization ability are also better. In terms of methodology, this study first innovatively improves the traditional Grey Wolf Optimization (GWO) algorithm to enhance its convergence and search ability. Specific improvement measures include implementing the reverse learning strategy to reduce the initial dependence of the algorithm on population distribution, and adding exploration perception strategy to enhance the global search ability of the algorithm by calculating heuristic factors, so as to identify the global optimal solution more effectively. The results showed that the improved W-DH-ELM model had excellent performance on multiple standard data sets. In particular, the average accuracy was more than 90%, which was significantly higher than other benchmark classification models. In terms of operation efficiency, the running time of the new model on different data sets was significantly reduced, accounting for less than 25%, and the lowest running time was only 4.89%. These experimental indicators verify the effectiveness of the introduced intelligent optimization algorithm in improving the performance of traditional ELM model without changing the original model structure. The improved W-DH-ELM model not only maintains the fast training characteristics of ELM, but also has higher accuracy and stability, which shows its superiority in dealing with complex classification tasks. In summary, the weighted double hidden layer extreme learning machine optimized by the improved GWO proposed in this study has significant advantages in classification problems, providing a new perspective for future machine learning applications and research.

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Improved Sparrow Search Algorithm with the Extreme Learning Machine and Its Application for Prediction

Cited by 12 publications

References 30 publications

Time difference of arrival location method based on improved dung beetle optimizer algorithm

Time difference of arrival location method based on improved dung beetle optimizer algorithm

Improved extreme learning machine based on adaptive dual-strategy optimization algorithm and its application

Optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

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