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International Core Journal of Engineering 2022年第7期8卷 846-859页

作者： Gang Yang

The standard sparrow search algorithm has some problems in solving high-dimensional function optimization problems, such as complex implementation process, slow convergence speed, poor global optimization ability and so on. In this paper, an improved sparrow search algorithm, MISSA, is proposed by using chaos map initialization elite population strategy, periodic adaptive convergence factor strategy, follower and early warning position update mode adjustment and polynomial mutation stagnation disturbance. The quality of the initial population is greatly improved by chaotic mapping elite population, The accuracy of the solution is improved; The adaptive convergence factor is used to accelerate the convergence speed of the algorithm; The adjustment of the location update mode of followers and early warning persons focuses on optimizing the specific operation of individual sparrow in the iterative process to simplify the implementation difficulty; Some mutation factors are introduced into the algorithm to increase its ability to jump out of local extremum, Thus, the standard SSA algorithm is improved in many aspects. Compared with the classical sparrow algorithm, the performance of the improved sparrow algorithm is faster than that of the sparrow algorithm, The four strategies are proved to be superior and effective.

关键词： sparrow search algorithm Chaotic Elite Population Adaptive Convergence Factor Polynomial Variation Function Optimization

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Short-term load forecasting with bidirectional LSTM-attention based on the sparrow search optimisation algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2023年第1期26卷 20-27页

作者： Wen, Jiahao Wang, Zhijian Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou Peoples R China

Aiming at the complexity and diversity of short-term power load data, a bidirectional long short-term memory (BILSTM) prediction model based on attention was proposed for the pretreatment collected data, and the different kinds of data were divided to obtain a training set and test set. The BILSTM layer was used for modelling to enable the extraction of the internal dynamic change rules of features and reduce the loss of historical information. An attention mechanism was used to give different weights to the implied BILSTM states, which enhanced the influence of important information. The sparrow search (SS) algorithm was used to optimise the hyperparameter selection process of the model. The test results showed that the performance of the proposed method was better than that of the traditional prediction model, and the root mean square errors (RMSEs) decreased by (1.18, 1.09, 0.60, 0.54) and (2.11, 0.45, 0.21, 0.11) on different datasets.

关键词： short-term load prediction sparrow search algorithm neural network weight assignment attention mechanism

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Discrete sparrow search algorithm for symmetric traveling salesman problem

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APPLIED SOFT COMPUTING 2022年第0期118卷 108469-108469页

作者： Zhang, Zhen Han, Yang Shanghai Univ Sch Mech Engn & Automat Shanghai 200444 Peoples R China

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. This paper proposes a swarm intelligence approach using a discrete sparrow search algorithm (DSSA) with a global perturbation strategy to solve the problem. Firstly, the initial solution in the population is generated by the roulette-wheel selection. Secondly, the order-based decoding method is introduced to complete the update of the sparrow position. Then, the global perturbation mechanism combined with Gaussian mutation and swap operator is adopted to balance exploration and exploitation capability. Finally, the 2-opt local search is integrated to further improve the quality of the solution. Those strategies enhance the solution & rsquo;s quality and accelerate the convergence. Experiments on 34 TSP benchmark datasets are conducted to investigate the performance of the proposed DSSA. And statistical tests are used to verify the significant differences between the proposed DSSA and other state-of-the-art methods. Results show that the proposed method is more competitive and robust in solving the TSP. (C) 2022 Elsevier B.V. All rights reserved.

关键词： TSP Swarm intelligence sparrow search algorithm Global perturbation 2-opt

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PV Power Forecasting Based on Relevance Vector Machine with sparrow search algorithm Considering Seasonal Distribution and Weather Type

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ENERGIES 2022年第14期15卷 5231页

作者： Ma, Wentao Qiu, Lihong Sun, Fengyuan Ghoneim, Sherif S. M. Duan, Jiandong Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Guilin Univ Elect Technol Guangxi Wireless Broadband Commun & Signal Proc K Guilin 541004 Peoples R China Taif Univ Coll Engn Dept Elect Engn POB 11099 Taif 21944 Saudi Arabia

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.

关键词： PV power prediction relevance vector machine sparrow search algorithm seasonal distribution and weather type

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An Apple Fungal Infection Detection Model Based on BPNN Optimized by sparrow search algorithm

BIOSENSORS-BASEL

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BIOSENSORS-BASEL 2022年第9期12卷 692页

作者： Zhao, Changtong Ma, Jie Jia, Wenshen Wang, Huihua Tian, Hui Wang, Jihua Zhou, Wei Beijing Informat Sci & Technol Univ Mech Elect Engn Sch Beijing 100192 Peoples R China Beijing Acad Agr & Forestry Sci Inst Qual Stand & Testing Technol Beijing 100097 Peoples R China Beijing Vocat Coll Agr Dept Food & Bioengn Beijing 102206 Peoples R China Hebei Food Inspect & Res Inst Hebei Food Safety Key Lab Shijiazhuang 050091 Hebei Peoples R China

To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization-back-propagation neural network (PSO-BPNN), a gray wolf optimization-backward propagation neural network (GWO-BPNN), and a sparrow search algorithm-backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.

关键词： electronic nose fungal infection sparrow search algorithm apples

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A Novel Lidar Signal-Denoising algorithm Based on sparrow search algorithm for Optimal Variational Modal Decomposition

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REMOTE SENSING 2022年第19期14卷 4960-4960页

作者： Li, Zhiyuan Li, Shun Mao, Jiandong Li, Juan Wang, Qiang Zhang, Yi North Minzu Univ Sch Elect & Informat Engn North Wenchang Rd Yinchuan 750021 Ningxia Peoples R China Key Lab Atmospher Environm Remote Sensing Ningxia North Wenchang Rd Yinchuan 750021 Ningxia Peoples R China

Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor alpha values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method.

关键词： lidar sparrow search algorithm variational modal decomposition singular value decomposition noise reduction

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A new hybrid method based on sparrow search algorithm optimized extreme learning machine for brittleness evaluation

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JOURNAL OF APPLIED GEOPHYSICS 2022年 207卷

作者： Zhang, Fengjiao Deng, Shaogui Zhao, Hui Liu, Xiang China Univ Petr East China Sch Geosci Qingdao 266580 Peoples R China Minist Educ Key Lab Deep Oil & Gas Geol & Explorat Qingdao 266580 Peoples R China Jilin Oilfield Res Inst Petr Explorat & Dev Songyuan 138000 Peoples R China

Shale brittleness is the key parameter to optimize reservoir fracturing modification and improve productivity. However, conventional methods based on rock mechanics and mineralogical parameters are limited by the lack of shear wave velocity and mineral composition data. Machine learning algorithms have been applied in the field of geophysical exploration, but the prediction accuracy of existing algorithms needs to be improved. In this study, a new hybrid model, an integration of the sparrow search algorithm (SSA) and the Extreme Learning Machine (ELM), named SSA-ELM, is proposed for predicting the brittleness index. ELM is performed to create the original brittleness model, meanwhile, the SSA algorithm is utilized to automatically explore the hyper -parameters of the model. Additionally, we also use 12 other favorite machine learning algorithms to validate the superiority of the proposed model combined with traditional logs and XRD-derived brittleness. 82 mineral brittleness databases from 5 exploration wells in the Songliao Basin, China were established. The simulation results of two Test wells indicate that the SSA-ELM model has the most accurate predictions and excellent generalization capability. The brittle profiles established based on the SSA-ELM model can effectively guide the fracturing exploration of shale oil reservoirs.

关键词： Brittleness index Shale oil sparrow search algorithm Extreme learning machine

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Optimization of an extreme learning machine model with the sparrow search algorithm to estimate spring maize evapotranspiration with film mulching in the semiarid regions of China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 201卷

作者： Jia, Yue Su, Yongjun Zhang, Ran Zhang, Zhaona Lu, Yukuo Shi, Dongxu Xu, Chaoqian Huang, Dongfeng Hebei Univ Water Resources & Elect Engn Cangzhou 061001 Peoples R China Remote Sensing & Smart Water Innovat Ctr Cangzhou 061001 Peoples R China Hebei Univ Ctr Water Automation & Informat Applicat Technol Cangzhou 061001 Peoples R China Botou Grand Canal Water Conservancy Construction C Cangzhou Peoples R China

Evapotranspiration (ET) is the crucial parameter of agricultural irrigation and the hydrological cycle. To obtain the optimal estimation model of ET with film-mulching for spring maize, the extreme learning machine model (ELM) optimized by sparrow search algorithm (SSA) was built. The ET results were compared with four machine learning models, including artificial bee colony algorithm optimized ELM model (ABC-ELM), particle swarm algorithm optimized ELM model (PSO-ELM), genetic algorithm optimized ELM model (GA-ELM), ELM model, and two empirical models, including the modified Shuttleworth-Wallace model (SW) and Priestley-Taylor model (PT). We evaluated the accuracy of different models using the root mean square error (RMSE), coefficient of determination (R-2), mean absolute error (MAE), coefficient of efficiency (E-ns) and GPI. The results showed that the SSA-ELM models show high accuracy under different input combinations in different growth periods. Throughout the growing season of spring maize, the slope of the fitting equation of the SSA-ELM9 model was 0.895. The RMSE, R-2, E-ns, MAE and GPI were 0.433 mm/d, 0.895, 0.895, 0.342 mm/d and 1.382, respectively. The SSA-ELM models showed the highest accuracy for ET estimation of spring maize in different growth periods, followed by PSO-ELM, ABC-ELM and GA-ELM models. The accuracy of the SSA-ELM models was better than that of the SW PT models. Therefore, the SSA-ELM model can estimate spring maize ET with film mulching.

关键词： Spring maize Film mulching Evapotranspiration Extreme learning machine sparrow search algorithm

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Spatiotemporal assessment of water security in the Dongting Lake region: Insights from projection pursuit method and sparrow search algorithm

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JOURNAL OF CLEANER PRODUCTION 2022年 378卷

作者： Xiang, Jiayi Tan, Shuai Tan, Xinlong Long, Jingwei Xiao, Tianlong Wang, Wei Hunan Agr Univ Coll Water Resources & Civil Engn Changsha 410128 Peoples R China Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming 650500 Peoples R China China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China

The Dongting Lake region (TDLR) is China's essential commodity grain base and inland wetland reserve. Few studies, however, focus on the water security of TDLR. Additionally, as the attributes of water security increase, common methods may not obtain more structural characteristics or make full use of the data, resulting in the evaluation not being comprehensive enough. Hence, based on the vulnerability scoping diagram (VSD) and the coupled model combining projection pursuit method (PP), sparrow search algorithm (SSA), and contribution degree model, this study assesses the water security of TDLR and its three major cities. The result shows that the coupled model may be suitable to assess water security since it obtains a more accurate, comparable, sensitive, comprehensive evaluation more rapidly, intuitively, and stably. The water security of TDLR and three cities all exhibit an overall fluctuating upward trend during 2011-2020, with a significant increase in the comprehensive water security evaluation value (WSV) of TDLR < 0.05). Among the three cities, the water security of Yiyang is the safest, followed by Changde and Yueyang. A summary of the main contributing dimensions and top ten contributing indicators of water security of TDLR illustrates that the effective measures to improve its water security include building embankments and sewage treatment plants through increased investment;reducing industrial water consumption;improving the ability to convert rainwater to water resources and surface water storage capacity. Additional measures, such as strengthening the integrated management of the Dongting Lake and increasing the use of reclaimed water for ecological replenishment, can effectively improve its water security. This study objectively assesses and summarizes the water security of TDLR and provides a valuable reference for relevant authorities in regional water resources planning and management, as well as for water security assessments of other similar l

关键词： Water security The Dongting lake region (TDLR) sparrow search algorithm Projection pursuit method Vulnerability scoping diagram (VSD)

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Parameter Identification for Memristive Chaotic System Using Modified sparrow search algorithm

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FRONTIERS IN PHYSICS 2022年 10卷

作者： Xiong, Qi Shen, Jun Tong, Bing Xiong, Ying Hunan Univ Arts & Sci Int Coll Changde Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

A memristor is a non-linear element. The chaotic system constructed by it can improve its unpredictability and complexity. Parameter identification of a memristive chaotic system is the primary task to implement chaos control and synchronization. To identify the unknown parameters accurately and quickly, we introduce the Sine Pareto sparrow search algorithm (SPSSA), a modified sparrow search algorithm (SSA). in this research. Firstly, we introduce the Pareto distribution to alter the scroungers' location in the SSA. Secondly, we use a sine-cosine strategy to improve the producers' position update. These measures can effectively accelerate the convergence speed and avoid local optimization. Thirdly, the SPSSA is used to identify the parameters of a memristive chaotic system. The proposed SPSSA exceeds the classic SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC) in simulations based on the five benchmark functions. The simulation results of parameter identification of a memristive chaotic system show that the method is feasible, and the algorithm has a fast convergence speed and high estimation accuracy.

关键词： parameter identification sparrow search algorithm swarm intelligence memristive chaotic system pareto distribution sine cosine algorithm

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