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Magnetic object recognition with magnetic gradient tensor system heading-line surveys based on kernel extreme learning machine and sparrow search algorithm

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MEASUREMENT 2022年 203卷

作者： Li, Qingzhu Li, Zhining Shi, Zhiyong Fan, Hongbo Army Engn Univ PLA Dept Vehicle & Elect Engn Shijiazhuang Campus Shijiazhuang 050003 Peoples R China Army Engn Univ PLA 97 Heiping West Rd Shijiazhuang 050003 Heibei Peoples R China

We found that single heading-line surveys from magnetic gradient tensor system (MGTS) can be used to realize pattern recognition of magnetic objects, such as shape and posture, which can greatly improve the target detection efficiency compared with the two-dimensional grid measurement. Abandoning complex mathematical process, we measure and learn several training routes in advance, and use kernel extreme learning machine (KELM) and sparrow search algorithm (SSA) to recognize the target. The magnetic gradient tensor and its derived variables are analyzed for the sensitivity of the magnetization direction, and two types of characteristic attributes suitable for the target posture and shape categories are summarized. Time-domain waveform feature extraction from continuously sampled signals helps build datasets with corresponding category labels. Principal component analysis (PCA) is used to reduce feature dimensionality and improve classifier efficiency. Both simulation and experiment dataset have achieved 100% accurate recognition of the target posture and shape categories.

关键词： Magnetic gradient tensor Heading -line survey Magnetic target pattern recognition Kernel extreme learning machine sparrow search algorithm

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Heavy metal content prediction based on Random Forest and sparrow search algorithm

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JOURNAL OF CHEMOMETRICS 2022年第10期36卷 e3445-e3445页

作者： Chen, Ying Liu, Zhengying Xu, Chongxuan Zhao, Xueliang Pang, Lili Li, Kang Shi, Yanxin Yanshan Univ Sch Elect Engn Hebei Prov Key Lab Test Measurement Technol & Ins Qinhuangdao 066004 Hebei Peoples R China China Geol Survey Minist Nat Resources Ctr Hydrogeol & Environm Geol Geol Environm Monitoring Engn Technol Innovat Ctr Baoding Peoples R China

X-ray fluorescence (XRF) analysis is exceedingly suitable for detecting heavy metal contents in soil. In order to do that, an accurate prediction model based on XRF analysis is necessary. But in practice, the XRF spectral data is susceptible to moisture content in soil, which may lead to inaccurate prediction results. Accordingly, a new prediction model based on Random Forest Regression (RFR) and improved sparrow search algorithm (SSA) was proposed, which takes the variation of moisture content into consideration. At first, the XRF spectral data were obtained by experiment. Owing to the advantages of training speed and prediction ability, the RFR was employed to predict the heavy metal contents. In order to further improve the performance of RFR, the SSA was selected and improved with theory of good-point set, which can determine optimum hyper-parameters of RFR conveniently. It can be found by comparison that the proposed model outperforms other commonly used models.

关键词： prediction model Random Forest Regression soil moisture content sparrow search algorithm X-ray fluorescence analysis

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A novel swarm intelligence optimization approach: sparrow search algorithm

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2020年第1期8卷 22-34页

作者： Xue, Jiankai Shen, Bo Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai Peoples R China

In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows. Experiments on 19 benchmark functions are conducted to test the performance of the SSA and its performance is compared with other algorithms such as grey wolf optimizer (GWO), gravitational search algorithm (GSA), and particle swarm optimization (PSO). Simulation results show that the proposed SSA is superior over GWO, PSO and GSA in terms of accuracy, convergence speed, stability and robustness. Finally, the effectiveness of the proposed SSA is demonstrated in two practical engineering examples.

关键词： Swarm optimization sparrow search algorithm convergence speed stability and robustness

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Unmanned aerial vehicle trajectory planning based on enhanced sparrow search algorithm

Unmanned aerial vehicle trajectory planning based on enhance...

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第二十一届分布式计算及其应用国际学术研讨会

作者： Anru Tang Yian Liu Hailing Song Jiangnan University School of AI and Computer Science

In order to solve the problems of the original sparrow search algorithm(SSA) in the unmanned aerial vehicle(UAV) trajectory planning,such as low optimization accuracy and slow convergence speed,an enhanced sparrow search algorithm(ESSA) was ***,Halton sequence was used to initialize the population to increase the diversity of the population and improve the subsequent search accuracy of the ***,the quasireflection learning mechanism is introduced to improve the individual quality of the algorithm after each iteration,and improve the optimization accuracy and convergence speed of the *** improved algorithm is applied to the trajectory planning of the UAV,the results show that the flight cost of the UAV trajectory found by ESSA is lower and the convergence speed is faster.

关键词： sparrow search algorithm Unmanned aerial vehicle Trajectory planning Halton sequence Quasi-reflection learning

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An Improved Chaos sparrow search Optimization algorithm Using Adaptive Weight Modification and Hybrid Strategies

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IEEE ACCESS 2022年 10卷 96159-96179页

作者： Zhang, Xuan-Yu Zhou, Kai-Qing Li, Peng-Cheng Xiang, Yin-Hong Zain, Azlan Mohd Sarkheyli-Hagele, Arezoo Jishou Univ Sch Commun & Elect Engn Jishou 416000 Hunan Peoples R China Jishou Univ Lab Ethn Cultural Heritage Digitizat Wuling Mt Ar Jishou 416000 Hunan Peoples R China Univ Teknol Malaysia UTM Big Data Ctr Skudai 81310 Johor Malaysia Malmo Univ Internet Things & People Res Ctr Dept Comp Sci & Media Technol S-20506 Malmo Sweden

sparrow search algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum.

关键词： Statistics Sociology Optimization Chaos Standards search problems Convergence Adaptive weighting modification cubic chaos mapping levy flight reverse learning sparrow search algorithm

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Mayfly sparrow search Hybrid algorithm for RFID Network Planning

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IEEE SENSORS JOURNAL 2022年第16期22卷 16673-16686页

作者： Zhang, Jiangbo Zheng, Jiali Xie, Xiaode Lin, Zihan Li, Haoshuai Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techn Nanning 530004 Guangxi Peoples R China

The radio frequency identification (RFID) technology is frequently used for large-scale RFID network planning, which brings thought-provoking RFID network planning (RNP) problems such as the uncertain number of readers, high interference rate of readers, poor economic benefits, and weak robustness. In this paper, we propose a mayfly sparrow search hybrid algorithm with Circle chaotic map and Levy flight, which is named MASSA. MASSA maps out the position of the readers by adding the idea of the mayfly algorithm (MA) into the optimization process of the sparrow search algorithm (SSA). Circle chaos mapping and Levy flight are added in the SSA optimization process and the nonlinear inertia coefficients to balance the relationship between global search and local search. The MASSA is compared with SSA, Improved sparrow search algorithm (ISSA), MA, particle swarm optimization algorithm (PSO), and gray wolf algorithm (GWO). The first part of the experiment uses the CEC2017 test suite to evaluate algorithm performance and analysis strategy effectiveness. The results encourage the application of MASSA to solve RNP problems. In the second part, experiments were conducted with four objective functions of coverage, interference, power, and load balance. Two different experiments were carried out with fixed number of readers and flexible number of readers under random label conditions. Experimental results show that MASSA has good planning performance, better economy, and robustness.

关键词： Radiofrequency identification Interference Statistics Sociology Optimization Behavioral sciences Planning Radio frequency identification (RFID) RFID network planning (RNP) sparrow search algorithm mayfly algorithm

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Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm

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MEASUREMENT 2021年 185卷 110079-110079页

作者： Gai, Jingbo Zhong, Kunyu Du, Xuejiao Yan, Ke Shen, Junxian Harbin Engn Univ Coll Aerosp & Civil Engn Harbin 150001 Heilongjiang Peoples R China

In gear fault diagnosis, most current intelligent fault diagnosis methods show good classification performance for fault pattern recognition. However, when detecting fault severity, the difficulty of diagnosis is increased due to the high similarity between the monitoring signals, which requires improving the sensitivity, stability, and accuracy of diagnosis methods. To address this issue, a parameter-optimized deep belief network (DBN) based on sparrow search algorithm (SSA) is proposed for gear fault severity detection. Firstly, the initial DBN is trained by the labeled gear fault signals in different severities. Secondly, SSA is introduced to optimize the learning rate and the batch size of the initial DBN, so as to avoid the interference caused by selecting network parameters by subjective experience. Finally, the detection method of gear fault severity based on the improved DBN with the optimal parameter combination is constructed. The performance of the proposed method is evaluated by analyzing the gear datasets under five degrees of tooth-breaking fault, the results show that the average detection accuracy reaches over 96% with a standard deviation of 1.46%. Compared with other methods, it is proved that the proposed method has better feature extraction ability, stability, and accuracy for gear fault severity detection.

关键词： Gear Fault severity detection Deep belief network sparrow search algorithm Parameter optimization

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Wavefront-shaping focusing based on a modified sparrow search algorithm

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OPTIK 2021年 244卷 167516-167516页

作者： Zhou, Shenghang Xie, Hao Zhang, Chuncheng Hua, Yingzi Zhang, Wenhui Chen, Qian Gu, Guohua Sui, Xiubao Nanjing Univ Sci & Technol Jiangsu Lab Spectral Imaging & Intelligence Sense Nanjing 210094 Jiangsu Peoples R China

We demonstrate a novel optimization method for wavefront-shaping focusing based on phase modulation. The proposed modified sparrow search algorithm achieves a higher convergence rate and a larger enhancement than the sparrow search algorithm by introducing crossover and mutation operations. Through a simulation, the validity of the proposed method is confirmed by comparison with the result of the algorithm without a modification. Further results indicate that the proposed method is robust to noisy environments. In summary, simulation and experimental results show that the proposed method outperforms other well-known wavefront-shaping focusing algorithms. Our work may be helpful for potential applications of wavefront shaping as well as offers useful insights for the modification of existing algorithms for focusing use.

关键词： Scattering media Wavefront shaping Focusing sparrow search algorithm

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A stochastic configuration network based on chaotic sparrow search algorithm

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KNOWLEDGE-BASED SYSTEMS 2021年 220卷 106924-106924页

作者： Zhang, Chenglong Ding, Shifei China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Peoples Republ China Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China

Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCN is affected by the assignation and selection of some network parameters significantly sparrow search algorithm (SSA) is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of sparrow population. In this paper, a stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN. Firstly, chaotic sparrow search algorithm (CSSA) is designed which mainly utilizes logistic mapping, self-adaptive hyper-parameters, mutation operator to enhance the global optimization capability of SSA;Secondly, as the performance of SCN is related to regularization parameter r and scale factor lambda of weights and biases, then CSSA is employed to give better parameters for SCN automatically;Finally, 13 benchmark functions and several datasets are used to evaluate the performance of CSSA and CSSA-SCN respectively. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Stochastic configuration network sparrow search algorithm Chaotic Logistic mapping Optimization

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An Improved sparrow search algorithm 18

An Improved Sparrow Search Algorithm

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18th IEEE Int Symp on Parallel and Distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Song, Wei Liu, Song Wang, Xiaochun Wu, Weiguo Xi An Jiao Tong Univ Fac Elect & Informat Engn Xian Peoples R China

ISBN: (纸本)9781665414852

The sparrow search algorithm (SSA) is a relatively new swarm intelligence heuristic algorithm. It has fast convergence speed, strong optimization ability and more extensive application scenarios compared with traditional heuristic search methods. And thus, the SSA is attracting the attention of researchers in different fields. However, there are deficiencies of initial population quality, search ability, and population diversity in the SSA. Therefore, this paper proposes an improved sparrow search algorithm (ISSA). The ISSA uses skew tent map-based chaotic method to produce initial population for a higher quality of convergence. For the location update of the producer sparrows during the iterations, the ISSA introduces a non-linear decreasing weight, promoting both exploration and exploitation of the search space, to improve the convergence and search precision. And the mutation strategy is employed to update the location of the scrounger sparrows with lower energy and the chaotic search is combined with the local exploitation for the scroungers with higher energy, which can enhance the diversity and avoid trapping in local optimum. Simulation experiments are carried out on 26 benchmark test functions. And the results show that the ISSA is superior to or at least competitive to the SSA in the convergence properties of accuracy, speed, and stability.

关键词： swarm intelligence algorithm sparrow search algorithm chaotic search non-linear decreasing weight convergence accuracy

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