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A New Hybrid Cryptocurrency Returns Forecasting Method Based on Multiscale Decomposition and an Optimized Extreme Learning Machine Using the sparrow search algorithm

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IEEE ACCESS 2022年 10卷 60397-60411页

作者： Du, Xiaoxu Tang, Zhenpeng Wu, Junchuan Chen, Kaijie Cai, Yi Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Fujian Agr & Forestry Univ Sch Econ & Management Fuzhou 350002 Peoples R China Nanchang Univ Sch Econ & Management Nanchang 330031 Jiangxi Peoples R China

The return series of cryptocurrencies, which are emerging digital assets, exhibit nonstationarity, nonlinearity, and volatility clustering compared to other traditional financial markets, making them exceptionally difficult to forecast. Therefore, accurate cryptocurrency price forecasting is essential for market participants and regulators. It has been demonstrated that improved data forecasting accuracy can be achieved through decomposition, but few researchers have performed information extraction on the residual series generated by data decomposition. Based on the construction of a "decomposition-optimization-integration" hybrid model framework, in this paper, we propose a multi-scale hybrid forecasting model that combines the residual components after primary decomposition for secondary decomposition and integration. This model uses the variational modal decomposition (VMD) method to decompose the original return series into a finite number of components and residual terms. Then, the residual terms are decomposed, and the features are extracted using the completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. The components are predicted by an extreme learning machine optimized by the sparrow search algorithm, and the final predictions are summed to obtain the final results. Forecasts for the returns of Bitcoin and Ethereum, which are significant cryptocurrency assets, are compared with other benchmark models constructed based on different ideas. We find that the proposed quadratic decomposition VMD-Res.-CEEMDAN-SSA-ELM hybrid model demonstrates the optimal and most stable forecasting performance in both one-step and multi-step ahead prediction of the cryptocurrency return series.

关键词： Predictive models Cryptocurrency Forecasting Data models Prediction algorithms Time series analysis Solid modeling Cryptocurrency model selection decomposition-ensemble extreme learning machine sparrow search algorithm

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An improved binary sparrow search algorithm for feature selection in data classification

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NEURAL COMPUTING & APPLICATIONS 2022年第18期34卷 15705-15752页

作者： Gad, Ahmed G. Sallam, Karam M. Chakrabortty, Ripon K. Ryan, Michael J. Abohany, Amr A. Kafrelsheikh Univ Fac Comp & Informat Kafrelsheikh Egypt Zagazig Univ Fac Comp & Informat Zagazig Egypt Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra ACT 2601 Australia UNSW Canberra ADFA Sch Engn & IT Capabil Syst Ctr Canberra ACT Australia Capabil Associates Canberra Canberra ACT Australia

Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-hard problem in which the computational time increases exponentially with an increase in problem complexity. To tackle such a problem type, metaheuristic techniques have been opted by an increasing number of scholars. Herein, a novel meta-heuristic algorithm, called sparrow search algorithm (SSA), is presented. The SSA still performs poorly on exploratory behavior and exploration-exploitation trade-off because it does not duly stimulate the search within feasible regions, and the exploitation process suffers noticeable stagnation. Therefore, we improve SSA by adopting: i) a strategy for Random Re-positioning of Roaming Agents (3RA);and ii) a novel Local search algorithm (LSA), which are algorithmically incorporated into the original SSA structure. To the FS problem, SSA is improved and cloned as a binary variant, namely, the improved Binary SSA (iBSSA), which would strive to select the optimal or near-optimal features from a given dataset while keeping the classification accuracy maximized For binary conversion, the iBSSA was primarily validated against nine common S-shaped and V-shaped Transfer Functions (TFs), thus producing nine iBSSA variants. To verify the robustness of these variants, three well-known classification techniques, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF) were adopted as fitness evaluators with the proposed iBSSA approach and many other competing algorithms, on 18 multifaceted, multi-scale benchmark datasets from the University of California Irvine (UCI) data repository. Then, the overall best-performing iBSSA variant for each of the three classifiers was com

关键词： Machine learning Classification Feature selection Meta-heuristics sparrow search algorithm Combinatorial optimization Swarm-based optimization Pattern recognition Data mining Binary optimization

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Intelligent quantification of natural gas pipeline defects using improved sparrow search algorithm and deep extreme learning machine

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CHEMICAL ENGINEERING REsearch & DESIGN 2022年 183卷 567-579页

作者： Xiong, Jingyi Liang, Wei Liang, Xiaobin Yao, Junming China Univ Petr Coll Safety & Ocean Engn 18 Fuxue Rd Beijing 102249 Peoples R China CNPC Tubular Goods Res Inst 89 Jinye Second Rd Xi'an Shaanxi Peoples R China

Accurate damage degree prediction is important to ensure the safety, stability, and economic operation of the natural gas pipeline in service, especially those in long-term service. In order to maintain the efficiency and safety of natural gas pipeline transportation, the intelligent diagnosis and evaluation technology of pipeline is very important. In this paper, The opposition-based learning strategy and adaptive T-distribution mutation operator are introduced to optimize the sparrow search algorithm (SSA),improve the search ability,convergence speed and accuracy of SSA algorithm. 12 classical benchmark functions are used to evaluate the performance of the improved algorithm. Experimental results demonstrate the feasibility and validity of ISSA compared with the original sparrow search algorithm. Based on its excellent performance, ISSA is used to optimize the input layer weight and bias parameters of deep extreme learning machine (DELM). Therefore, a comprehensive ISSA-DELM network model is constructed for intelligent quantitative evaluation of natural gas pipeline defects. The results show that the model can effectively overcome the problem that the DELM effect is affected by the random input weight and random bias of each ELM-AE, and improve the quantitative prediction performance. This will facilitate the assessment of the integrity and safety status of natural gas pipelines. (c) 2022 Published by Elsevier Ltd on behalf of Institution of Chemical Engineers.

关键词： Natural gas pipeline sparrow search algorithm Deep extreme learning machine Adaptive T-distribution Opposition-based learning Defects quantification

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Short-term wind speed forecasting based on two-stage preprocessing method, sparrow search algorithm and long short-term memory neural network

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ENERGY REPORTS 2022年 8卷 14997-15010页

作者： Ai, Xueyi Li, Shijia Xu, Haoxuan Wuhan Univ Sci & Technol Evergrande Sch Management Wuhan 430070 Hubei Peoples R China Zhongnan Univ Econ & Law Sch Business Adm Wuhan 430073 Hubei Peoples R China

Wind energy, as an environment-friendly and renewable energy source, has become one of the most effective alternatives to conventional power sources. However, the intermittent nature of wind speed and the interference of noise signal bring several challenges to the safety and reliability of power grid operation. To tackle this issue, a two-stage preprocessing strategy is designed, and the short-term wind speed prediction model based on long short-term memory (LSTM) is proposed. Firstly, singular spectrum analysis (SSA) is introduced to extract the target data and filter the noise data. Next, the denoised sequence is decomposed by variational mode decomposition (VMD) into multiple intrinsic mode functions (IMFs), which are further aggregated by sample entropy (SE). Besides, the hyper-parameters of LSTM neural network are optimized by the newly sparrow search algorithm (SPSA) possessing excellent global optimization ability. Subsequently, the aggregated sequences are coupled with the SPSA-LSTM modules synchronously. The ultimate wind speed forecasting results are obtained by superimposing the predicted values of all sequences. In order to evaluate the effectiveness of proposed approach, two case studies are conducted based on two datasets collected from different sites with 10-min and 1-hour intervals by comparing seven relevant models. The experimental results demonstrate that the proposed SSA-VMD-SE-SPSA-LSTM can adequately extract the inherent features of wind speed series, thus achieving higher prediction accuracy. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Short-term wind speed prediction Singular spectrum analysis Variational mode decomposition Sample entropy Long short-term memory sparrow search algorithm

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Water Supply Network Optimization based on Improved sparrow search algorithm 12

Water Supply Network Optimization based on Improved Sparrow ...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Huang, Xiaoyi Wang, Yungan Chu, Jizheng Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9798350321050

The water supply network is an essential infrastructure for urban life, and designing a scientific and reasonable water supply network can not only reduce construction and operation and maintenance costs but also enhance the reliability of the system. However, the design of the system involves complex constraints, as well as optimizing the pipe network is a nonlinear problem that significantly impacts construction investments. Traditional optimization methods have demonstrated poor performance in pipe network optimization due to low convergence accuracy and weak global search capabilities. To address these shortcomings, this paper proposes an Improved sparrow search algorithm (ISSA) that introduces a Levy flight strategy to increase spatial search diversity and eliminate the small search space in the late iteration period and random walk around the optimal solution. The improved algorithm is applied to Hanoi, New York, and ZJ pipe networks, showing that it can efficiently find the best cost design scheme while reducing calculation cost and realizing rapid optimization of the combination optimization problem.

关键词： Water supply network optimization sparrow search algorithm Improved algorithm

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Energy efficient cluster head selection using improved sparrow search algorithm in Wireless Sensor Networks

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 8564-8575页

作者： Kathiroli, Panimalar Selvadurai, Kanmani Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry India Pondicherry Engn Coll Dept Informat Technol Pondicherry India

Wireless Sensor Networks include a substantial number of nodes with limited battery power that is used for gathering and sending the data to the Base station. Here most energy is consumed in data transfer. Hence a foremost problem is the maximization of network lifetime through minimization of energy consumption in the nodes. To resolve this issue, the clustering method is involved to achieve energy-efficient data transmission. A cluster head is elected which uses an energy distribution mechanism to conserve the remaining power, thereby extending network lifetime. Various present methodologies are employed but every algorithm had notable constraints individually. In this paper, a hybrid sparrow search algorithm with Differential Evolution algorithm is intended to solve the energy efficiency issue by cluster head selection in Wireless Sensor Networks. The proposed algorithm uses the high-level search efficiency of the sparrow search algorithm and the lively potential of Differential Evolution that enhances the lifetime of nodes. The performance of this hybrid model seems to be exploiting the count of alive nodes and dead nodes, throughput, and residual energy. The proposed Improved sparrow search algorithm using the Differential evolution model for choosing the best possible cluster head shows a development in residual power and throughput than other compared algorithms. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Wireless Sensor Network Clustering Differential evolution sparrow search algorithm Network lifetime Selvadurai)

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Improved Gated Recurrent Unit Network with sparrow search algorithm for Lithium-ion Battery State of Health Estimation

Improved Gated Recurrent Unit Network with Sparrow Search Al...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Liu, Xinghua Gao, Zhichao Tian, Jiaqiang Ma, Tianyu Li, Siqi Yin, Jianning Zhang, Qingping Xian Univ Technol Sch Elect Engn Xian Peoples R China Anhui Univ Sch Elect Engn & Automat Hefei Peoples R China State Grid Ningxia Elect Power Co Ltd Power Res Inst Yinchuan Ningxia Peoples R China

ISBN: (纸本)9798350339345

The condition of lithium-ion batteries (LIBs), commonly referred to as the state of health (SOH), holds a critical role in ensuring the safety of the entire system. A precise estimation of SOH is *** paper introduces the SSAGRU network, a battery SOH estimation method that utilizes the sparrow search algorithm and an improved gated recurrent unit. The current curve is used to extract three different features related to battery aging during the constant voltage stage, and their correlation with SOH is analyzed through grey correlation analysis. The proposed SSA-improved GRU model proves to be effective in achieving accurate and stable SOH estimation, outperforming two other algorithms tested.

关键词： Lithium-ion batteries Feature extraction Gated Recurrent Unit neural network sparrow search algorithm

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RBF neural network modeling and application based on improved sparrow search algorithm 35

RBF neural network modeling and application based on improve...

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35th Chinese Control and Decision Conference (CCDC)

作者： song, Jian Cong, Qiumei Jiang, Xongqiu Zhang, Mengyan Yang, Jian Yang, Shuaishuai Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Peoples R China

ISBN: (纸本)9798350334722

Using sparrow search algorithm (SSA) to optimize neural network model is easy to fall into local extremum, which leads to low accuracy of soft sensor. To solve this problem, this paper proposes an improved sparrow search algorithm (ISSA) to optimize the widths, centers and weights of radial basis function neural network (RBFNN). Taking the mean square error value as the individual fitness value, the position update formulas of discoverers, participants and early warning persons in SSA are improved to optimize the parameters of RBFNN. Finally, the convergence of the algorithm was proved, and the chemical oxygen demand (COD) was predicted using the random data validation and the measured data of the sewage treatment plant. The results showed that the mean absolute percentage error of SSA-RBF neural network was 1.55%, the mean absolute percentage error of ISSA-RBF neural network was 1.26%, and the mean absolute percentage error decreased by 0.29%, It shows that ISSA-RBF neural network has high soft-sensing accuracy.

关键词： sewage treatment RBF neural network sparrow search algorithm soft sensor

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sparrow search algorithm for Solving Flexible Jobshop Scheduling Problem 12th

Sparrow Search Algorithm for Solving Flexible Jobshop Schedu...

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12th International Conference on Swarm Intelligence (ICSI)

作者： Wu, Mingliang Yang, Dongsheng Yang, Zhile Guo, Yuanjun Northeastern Univ Intelligent Elect Sci & Technol Res Inst Boston MA 02115 USA Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China

ISBN: (纸本)9783030787424;9783030787431

With the global development of the third industrial revolution, intelligent manufacturing has received attention from many countries and regions since it was first proposed. In the next ten years, intelligent manufacturing has become an important factor in determining international status, and it is imminent for traditional manufacturing to switch to intelligent manufacturing. Flexible job-shop scheduling is a key research problem in the field of intelligent manufacturing. In this paper, we uses a novel swarm intelligence optimization algorithm-sparrow search algorithm to solve the problem of the longest processing time of workshop scheduling. The experimental results show that compared with other advanced meta-heuristic algorithms, the sparrow search algorithm (SSA) can not only achieve ideal optimization accuracy in the test function, but also can achieve acceleration effects and solving capabilities that other algorithms do not have in actual shop scheduling problems.

关键词： sparrow search algorithm Flexible job shop scheduling problem Makespan Global convergence

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An improved sparrow search algorithm based on fractional calculus and its application to the PID control 35

An improved sparrow search algorithm based on fractional cal...

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35th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Xiaomeng Ge, Fudong Zhang, Shuo Liu, Lu Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang Peoples R China China Univ Geosci Sch Comp Sci Wuhan Peoples R China Northwestern Polytech Univ Sch Math & Stat Xian Peoples R China Northwestern Polytech Univ Sch Marine Sci Technol Xian Peoples R China

ISBN: (纸本)9798350334722

This paper studies the sparrow search algorithm based on fractional calculus. When the original algorithm cannot find the optimal value accurately, this method can effectively balance the search speed and improve the convergence accuracy. To this end, we consider the unique heritability of fractional calculus, which can retain the previous memory features well, with strong correlation and balance. The algorithm convergence of the speed can be considerably improved with the addition of adaptive weight. The performance test on 6 benchmark functions with different characteristics shows that the optimization ability of the new algorithm is obviously better than that of the original algorithm. The improved sparrow search algorithm is then used to adjust the parameters of the fractional order PID controller. Improved simulation results illustrate that this method can effectively obtain the optimal controller parameters.

关键词： sparrow search algorithm fractional calculus adaptive weight PID control

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