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Multi-UAV Path Planning Based on Fusion of sparrow search algorithm and Improved Bioinspired Neural Network

IEEE ACCESS 2021年 9卷 124670-124681页

作者： Liu, Qingli Zhang, Yang Li, Mengqian Zhang, Zhenya Cao, Na Shang, Jiale Dalian Univ Commun & Network Lab Dalian 116622 Peoples R China

Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles, and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of sparrow search algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision-free path in a mountainous environment, and the planned path is smooth and short. Compared with the Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA), the fusion algorithm has obvious advantages in the stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.

关键词： Heuristic algorithms Path planning Unmanned aerial vehicles Vehicle dynamics Neurons Planning Optimization Path planning multi-UAV sparrow search algorithm bioinspired neural network safe surface

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Medical image contrast enhancement based on improved sparrow search algorithm

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023年第1期33卷 389-402页

作者： Fan, Xiaoyan Sun, Zhanquan Tian, Engang Yin, Zhong Cao, Gaoyu Univ Shanghai Sci & Technol Coll Sci Shanghai Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

The collection or transmission of medical images is often disturbed by various factors, such as insufficient brightness and noise pollution, which will result in the deterioration of image quality and significantly affect the clinical diagnosis. To improve the quality of medical images, a contrast enhancement method based on improved sparrow search algorithm is proposed in this paper. The method is divided into two steps to enhance the medical images. First, a new transform function is introduced to improve the brightness or contrast of medical images, and two parameters in the transform function are optimized by the improved sparrow search algorithm. Second, adaptive histogram equalization method with contrast limited is used to equalize the result image of the previous step to make the pixel distribution of the image more uniform. Finally, a large number of experiments and qualitative and quantitative analyses were conducted on the common data sets. The analysis results demonstrate that the presented approach outperforms some existing medical image processing approaches.

关键词： histogram equalization incomplete Beta function medical image enhancement sparrow search algorithm

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A multi-strategy-guided sparrow search algorithm to solve numerical optimization and predict the remaining useful life of li-ion batteries

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JOURNAL OF SUPERCOMPUTING 2024年第11期80卷 16254-16300页

作者： Xue, Jiankai Shen, Bo Pan, Anqi 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 optimization method is proposed based on the sparrow search algorithm, namely, multi-strategy-guided sparrow search algorithm (MGSSA). It is well-known that the basic SSA has limitations such as the slow convergence speed and vulnerability to local optimality. Surrounding these two issues, some strategies are presented in the MGSSA. Firstly, the newly introduced ring topology search strategy not only maintains the diversity of the entire population but also enhances the exploration ability of the SSA. Secondly, the proposed leader-based search strategy can improve exploitation ability of the SSA to prevent falling into local optimum as much as possible. Moreover, the coordinated learning strategy is put forward to better balance between the exploration and exploitation abilities. Finally, the MGSSA is compared with seventeen advanced algorithms on two well-known benchmark suites (i.e., CEC-2017 and CEC-2020). Meanwhile, the MGSSA-based forecasting approach is applied to predict the remaining useful life for lithium-ion batteries. The statistical results indicate that the MGSSA is a high-performance optimizer, which can not only solve the defects of the original SSA, but also obtain satisfactory solutions in both complex numerical optimization and real-world application problems.

关键词： Coordinated learning strategy Leader-based search strategy Ring topology search strategy Remaining useful life sparrow search algorithm

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Hydrological time series prediction by extreme machine learning and sparrow search algorithm

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WATER SUPPLY 2022年第3期22卷 3143-3157页

作者： Feng, Bao-fei Xu, Yin-shan Zhang, Tao Zhang, Xiao Changjiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China

In general, accurate hydrological time series prediction information is of great significance for the rational planning and management of water resource system. Extreme learning machine (ELM) is an effective tool proposed for the single-layer feedforward neural network in the regression and classification problems. However, the standard ELM model falls into local minimum with a high probability in hydrological prediction problems since the randomly assigned parameters (like input-hidden weights and hidden biases) often remain unchanged at the learning process. For effectively improving the prediction accuracy, this paper develops a hybrid hydrological forecasting model where the emerging sparrow search algorithm (SSA) is firstly used to determine the satisfying parameter combinations of the ELM model, and then the Moore-Penrose generalized inverse method is chosen to analytically obtain the weight matrix between the hidden layer and output layer. The proposed method is used to forecast the long-term daily runoff series collected from three real-world hydrological stations in China. Based on several performance evaluation indexes, the results show that the proposed method outperforms several ELM variants optimized by other evolutionary algorithms in both training and testing phases. Hence, an effective evolutionary machine learning tool is developed for accurate hydrological time series forecasting.

关键词： extreme machine learning hydrologic forecasting sparrow search algorithm

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Short-term power load forecasting based on sparrow search algorithm-variational mode decomposition and attention-long short-term memory

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1089-1097页

作者： Duan, Qinwei He, Xiangzhen Chao, Zhu Tang, Xuchen Li, Zugang Power Dispatch Guangdong Power Grid Control Ctr 75 Meihua Rd Guangzhou 510000 Guangdong Peoples R China Beijing Qingneng Internet Technol Co LTD 1 Longshi St Beijing 100084 Peoples R China

To improve the forecasting accuracy of power load, the forecasting model based on sparrow search algorithm (SSA), variational mode decomposition (VMD), attention mechanism and long short-term memory (LSTM) was proposed. Firstly, SSA is used to optimize the number of decomposition and penalty factor in VMD and realize the decomposition operation of the initial data. Then, LSTM is used to predict each component, and on this basis, feature and temporal attention mechanisms are introduced. Feature attention mechanism is introduced to calculate the contribution rate of relevant input features in real time, and the feature weights are modified to avoid the limitations of traditional methods relying on the threshold of expert experience association rules. Temporal attention mechanism is applied to extract the historical key moments and improve the stability of the time series prediction effect. Finally, the final result is obtained by superimposing the prediction results of each component to complete the power load prediction. Practical examples show that, compared with other methods, the proposed model achieves the highest prediction accuracy, with an RMSE of 1.23, MAE of 0.99 and MAPE of 11.62%.

关键词： load forecasting sparrow search algorithm variational mode decomposition attention mechanism long short-term memory

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期83卷 41597页

作者： Chen, Gang Zhu, Donglin Chen, Xiangyu Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China JiangXi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China

Although image detection technology is widely used in various fields, there is still a large gap in the similarity detection of science fiction painting. Therefore, a similarity detection method for science fiction painting is proposed. Firstly, a k-layer pyramid image is created for the source image to be detected and the template image. Then, multi-strategy improved sparrow algorithm (MISSA) is used to perform rough matching in the top subgraph of the source image to obtain the coordinates of the initial matching target, the location transferred from the layer above each layer is the starting point of search, and pixel by pixel matching is carried out within the set window range. Finally, pHash is used as the similarity measure to calculate the similarity of matching results. The hybrid search strategy based on step function, multi-stage dynamic control of safety threshold, and food search strategy based on Logistic model are used to improve sparrow search algorithm, thereby forming MISSA to improve the accuracy and real-time performance in the matching process. In terms of performance verification of MISSA, the rationality and effectiveness of the three improved strategies are verified by ablation experiment, and the experimental results on CEC2017 benchmark function show that the optimization performance and convergence performance of MISSA are better than that of peer algorithms. The comparison results in the similarity detection experiment of science fiction painting fully verify that the proposed detection method has strong robustness in meeting the requirements of real-time and accuracy of matching.

关键词： sparrow search algorithm Gaussian pyramid Multi-strategy improvements Science fiction painting Similarity detection

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Multi-Objective Optimization of Steel Pipe Pile Cofferdam Construction Based on Improved sparrow search algorithm

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10407页

作者： Jiang, Zaolong Yang, Chengfang Yue, Hongbo Hunan Univ Sch Civil Engn Changsha 410082 Peoples R China Hunan Huda Construct Supervis Co Ltd Changsha 410082 Peoples R China China Railway Seventh Bur Grp Third Engn Co Ltd Xian 710032 Peoples R China

This paper develops a multi-objective optimization model to address the absence of systematic and practical evaluation methods for selecting construction schemes for steel pipe pile cofferdams. The model aims to minimize duration and cost while maximizing quality. Additionally, it proposes an improved sparrow search algorithm (ISSA) to solve this problem. First, a tent chaotic map is introduced to initialize the sparrow population, enhancing the diversity of the initial population. Second, the principle of non-dominated ordering is introduced to sort the parent and offspring populations during the iteration process, and the appropriate individuals are selected to form the offspring population. Finally, gray correlation analysis is applied to optimize the Pareto solution set and determine the final construction scheme. The effectiveness and superiority of the ISSA is verified by using the Changsha Jinan Avenue project as a case study. The results indicate that the quality of the optimized construction scheme remains at a high level of 0.90 or more;the duration is shortened by 18 days, a reduction of 21%;and the total cost is reduced by CNY 220,000, saving 3% of the cost.

关键词： sparrow search algorithm multi-objective optimization steel pipe pile cofferdam non-dominated sorting tent chaotic map

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Software defect prediction ensemble learning algorithm based on adaptive variable sparrow search algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第6期14卷 1967-1987页

作者： Tang, Yu Dai, Qi Yang, Mengyuan Du, Tony Chen, Lifang North China Univ Sci & Technol Coll Sci Tangshan 063210 Hebei Peoples R China China Univ Petr Coll Informat Sci & Engn Beijing 102249 Peoples R China Bradley Univ CSIS Dept 1501 W Bradley Ave Peoria IL 61625 USA Coll Sci Hebei Key Lab Data Sci & Applicat Tangshan 063210 Hebei Peoples R China

Software defect prediction has caused widespread concern among software engineering researchers, which aims to erect a software defect prediction model according to historical data. Among all the techniques used in this field, extreme learning machine is widely used by researchers because of its simple structure and excellent learning speed. At the same time, the prediction performance of extreme learning machine is greatly affected by the random selection of parameters and the weak generalization ability. In this sense, in order to improve the prediction performance of the model, researchers uses swarm intelligence optimization algorithm to provide the optimal parameters for the model. sparrow search algorithm is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of the sparrow group. However, the original sparrow search algorithm is easily trapped to local optimal solutions in the later stage of the iterations. To improve the global optimization ability of the original sparrow search algorithm, this paper proposed an adaptive variable sparrow search algorithm (AVSSA) based on adaptive hyper-parameters and variable logarithmic spiral. This work run experiments of AVSSA in eight benchmark functions, and obtained the satisfactory results. In the traditional software defect prediction algorithm, the imbalance of data distribution is also one of the main reasons that affect the performance of the model. Therefore, this paper uses the adaptive variable sparrow search algorithm to optimize the extreme learning machine as the base predictor for Bagging ensemble learning (AVSEB). A new software defect prediction ensemble learning model is proposed in this paper. Firstly, the model used the unstable cut-points algorithm to preprocess Bagging sample set in this model. Then, the adaptive variable sparrow search algorithm is used to optimize the extreme learning machine as the base predictor of ensemble learning. Finally, the voting method i

关键词： sparrow search algorithm Adaptive hyper-parameters Variable logarithmic spiral Extreme learning machine Ensemble learning Software defect prediction

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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Karim, Faten Khalid Varela-Aldas, Jose Ishak, Mohamad Khairi Aljarbouh, Ayman Mostafa, Samih M. Princess Nourah Bint Abdulrahman Univ PNU Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh 11671 Saudi Arabia Univ Tecnol Indoamer Fac Ingn Ctr Invest Ciencias Humanas & Educ CICHE Ingn Ind Ambato 180103 Ecuador Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn Ajman U Arab Emirates Univ Cent Asia Sch Arts & Sci Dept Comp Sci Naryn Kyrgyzstan South Valley Univ Fac Comp & Informat Dept Comp Sci Qena Egypt New Assiut Technol Univ NATU Fac Ind & Energy Technol New Asyut 71515 Egypt

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the sparrow search algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform.

关键词： Industrial internet of things Cyberattack detection Bayesian machine learning Chameleon optimization algorithm sparrow search algorithm

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Application of uniform experimental design theory to multi-strategy improved sparrow search algorithm for UAV path planning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Cheng, Lianyu Ling, Guang Liu, Feng Ge, Ming-Feng Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China

The sparrow search algorithm (SSA) is a meta-heuristic optimization algorithm based on the predatory behavior of sparrows. However, SSA tends to fall into the local optimum when solving optimization problems with complex constraints. To improve its optimization efficiency and overall performance, this paper develops a multi-strategy improved SSA (ISSA) based on uniform experimental design theory. Specifically, the wraparound L2-discrepancy (WD), as a uniformity metric for uniform design of experiments, is fully utilized with macro-regulation, adaptive dynamic management strategy, and boundary redistribution management mechanism to quantify the population uniformity in each iteration of ISSA. Inspired by the concept of uniform design, WD is initially adopted to gauge population uniformity, and the threshold acceptance (TA) algorithm is employed to produce an initial population with improved uniformity, hence augmenting the population's diversity and quickening the rate of convergence. Secondly, a macroscopical individual iterative strategy of the producer is adjusted to avoid the population converging to the origin. Then, a dynamic population uniformity affiliation function based on WD is introduced to adjust the population uniformity affiliation function according to the relative amount of change in the global optimum, and the number of danger perceivers is adjusted according to the affiliation function. What is more, a new boundary update strategy is also proposed in ISSA based on population uniformity. By comparing ISSA on 23 standard test functions and recently updated validation function set CEC2022 with the original SSA, and some classical as well as newly developed algorithms, the superiority of the present ISSA is thoroughly confirmed. As an application case, the ISSA algorithm is utilized to solve the path planning problem of the complex environment of unmanned aerial vehicle (UAV) based on threat models by applying it to 2D maps containing circular and poly

关键词： sparrow search algorithm Uniform design Dynamic affiliation function UAV path planning Wrap-around L2-discrepancy

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