Comparative Study and Improvement Analysis of Sparrow Search Algorithm

Yan, Shaoqiang; Liu, Weidong; Li, Xin-Qi; Yang, Ping; Wu, Fengxuan; Yan, Zhe

doi:10.1155/2022/4882521

Cited by 23 publications

(11 citation statements)

References 40 publications

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“…The SSA is a nascent optimization algorithm rooted in swarm intelligence principles [ 34 ] that learns from the behavioral strategies of sparrows, including foraging and anti-predation. The authors of [ 35 ] compared the performance of four emerging intelligent optimization algorithms: Grey Wolf optimization (GWO), particle swarm optimization (PSO), the differential evolution (DE) algorithm, and the SSA. Their experimental results indicated that the SSA exhibits strong local search ability under a variety of test function experiments, and has the advantages of high precision and fast convergence speed.…”

Section: Algorithm Principlesmentioning

confidence: 99%

Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting

Liu,

Kong

et al. 2024

Sensors

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Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning.

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Section: Algorithm Principlesmentioning

confidence: 99%

Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting

Liu,

Kong

et al. 2024

Sensors

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“…Using ( 2) or ( 12) to update the scroungers' position; (10) end for (11) for i � 1: SD do (12) Using equation ( 3) to update the scouters' position; (13) end for (14) for i � 1: N do (15) if the new position is better than the previous position then (16) Using the new position to update the previous position; (17) end if (18) if the new position is better than the optimal position then (19) Using the new position to update the optimal position; (20) end if (21) end for (22) t � t + 1 (23) end while (24) return f best , X best ALGORITHM 1: Te improved sparrow search algorithm. Computational Intelligence and Neuroscience Equation ( 13) can be expressed as a matrix as follows:…”

Section: Inputmentioning

confidence: 99%

“…Compared with other algorithms, SSA has some advantages in convergence speed and global search ability. Nevertheless, when solving complex problems, the performance of SSA is greatly afected by the initial population, and the diversity of the population will decrease signifcantly with the iterative process [17]. In addition, in the optimization process, the convergence accuracy of SSA needs to be improved, and the ability to jump out of the local optimal solution needs to be enhanced.…”

Section: Introductionmentioning

confidence: 99%

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

Yang

Tao

Zhou

et al. 2023

Computational Intelligence and Neuroscience

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The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm. It has a fast convergence speed and strong global search ability. However, SSA also has many shortcomings, such as the unstable quality of the initial population, easy to fall into the local optimal solution, and the diversity of the population decreases with the iterative process. In order to solve these problems, this paper proposes an improved sparrow search algorithm (ISSA). ISSA uses Chebyshev chaotic map and elite opposition-based learning strategy to initialize the population and improve the quality of the initial population. In the process of producer location update, dynamic weight factor and Levy flight strategy are introduced to avoid falling into a local optimal solution. The mutation strategy is applied to the scrounger location update process, and the mutation operation is performed on individuals to increase the diversity of the population. In order to verify the feasibility and effectiveness of ISSA, it is tested on 23 benchmark functions. The results show that compared with other seven algorithms, ISSA has higher convergence accuracy, faster convergence speed, and stronger stability. Finally, ISSA is used to optimize the sound field of high-intensity focused ultrasound (HIFU). The results show that ISSA can effectively improve the focusing performance and reduce the influence of sound field sidelobe, which is of great benefit for HIFU treatment.

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“…The sparrow search algorithm (Yan, et al, 2022) is a new intelligent algorithm that imitates sparrow foraging and predation. In the SSA algorithm, the discoverer, joiner and scout cooperate to carry out local search and global search.…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

Displacement prediction of open-pit mine slope based on SSA-ELM

Qiu²

2023

Front. Earth Sci.

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Mine geological disaster is a complex non-linear system. The traditional prediction model has the disadvantages of low prediction accuracy and poor reliability. In order to solve this problem, the open-pit mine slope displacement is taken as the research object. Based on a new algorithm extreme learning machine (ELM), the new intelligent algorithm sparrow search algorithm (SSA) are introduced to determine the weights and thresholds of the input layer and hidden layer of ELM. The open-pit mine slope displacement prediction model of improved ELM is constructed and applied to an engineering example. The results show that the root mean square error of SSA-ELM model is only a quarter of that of BP model, which is 50% higher than that of GM (1,1) and ELM models. The correlation coefficient of the prediction results of the SSA-ELM model is 0.983, and the accuracy is better than that of the traditional model. The single ELM model and the PSO-ELM model show that the SSA algorithm has better improvement effect. The SSA model has good comprehensive performance and high prediction accuracy. It is feasible to apply it to the prediction of slope displacement in open-pit mines.

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Comparative Study and Improvement Analysis of Sparrow Search Algorithm

Cited by 23 publications

References 40 publications

Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting

Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

Displacement prediction of open-pit mine slope based on SSA-ELM

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