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Proceedings of the 2023 International Conference on Mathematics, Intelligent Computing and Machine Learning

作者： Junfei Zheng Xu Liang Lingyan Hou Dali Yang Qiang Tong Beijing Information Science & Technology University China

ISBN: (纸本)9798400709258

In addressing the lot streaming hybrid flow shop problem with equipment adjustment times, a scheduling model is devised with the multi-objective of minimizing completion time and energy consumption. To tackle this intricate problem, we propose an improved multi-objective sparrow search algorithm (ISSA). The algorithm initiates the population through a three-stage variable-length real-number coding and Logistic-Tent chaotic mapping, enhancing the quality of initial solutions. Subsequently, a multifaceted enhancement is introduced during the sparrow's search phase to elevate population diversity. In the sparrow's explorer phase, an adaptive inertia weight is introduced. In the sparrow's follower phase, a subset of the population engages in both sine and cosine searches, while another subset adopts a spiral search strategy to follow the exploratory sparrows. This paper applies six optimization algorithms and designs a set of test instances to compare the performance of each algorithm. Comparative experiments validate the ability of ISSA to escape local optima and converge quickly.

关键词： Logistic-Tent chaos mapping hybrid flow shop scheduling lot streaming multi-objective optimization sparrow search algorithm

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Chaotic sparrow search algorithm with manta ray spiral foraging for engineering optimization

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2023年第1期11卷

作者： Yang, Chao Yang, Hong Zhu, Donglin Hu, YiWen Zhang, Yu Ma, HongYuan Huang, ZhiHong Shenyang Univ Coll Informat Engn Shenyang Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua Peoples R China

Targeting the problem of traditional sparrow search algorithms being prone to falling into local optima, a new algorithm called the Chaotic sparrow search algorithm with Manta Ray Spiral Foraging (abbreviated as MSSA) is proposed. The Logistic-Sine-Cosine chaotic map and elite Reverse learning strategy are fused to initialize the population. It is experimentally demonstrated that this hybrid strategy outperforms the population after random initialization in reducing ineffective sparrow individuals. In the vigilante update stage, the spiral foraging behaviour of the manta ray population in the integrated manta ray optimization algorithm, the sparrows search around the best food source, which enhances the sparrow search algorithm's ability to explore the optimal solution. To enhance the stability of the algorithm to search for the optimum, a mixed Gaussian variational and logistic perturbation strategy is proposed to further improve the performance of the algorithm. Finally, using 12 commonly used benchmark test functions and the Wilcoxon rank sum test, MSSA was compared with other original algorithms and advanced improved algorithms, it is demonstrated that MSSA has higher accuracy and convergence performance, and the improved MSSA algorithm is applied to three types of engineering optimization problems with constraints, demonstrating its feasibility and effectiveness.

关键词： sparrow search algorithm sparrow search algorithm mixed chaotic mapping mixed chaotic mapping elitereverse learning elitereverse learning manta rayspiral foraging manta rayspiral foraging Gaussianvariation Gaussianvariation chaoticperturbation chaoticperturbation

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Research on sparrow search Optimization algorithm for multi-objective task scheduling in cloud computing environment

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 10397-10409页

作者： Luo, Zhi-Yong Chen, Ya-Nan Liu, Xin-Tong Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking search Optimization algorithm (BSA) and the sparrow search algorithm (SSA). BSA enhances SSA's convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSAis evaluated and compared against traditionalSSAand other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

关键词： Cloud computing task scheduling multi-objective optimization sparrow search algorithm

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An Improved DV-Hop algorithm Optimized by Correcting the Average Hop Distance and the sparrow search algorithm

An Improved DV-Hop Algorithm Optimized by Correcting the Ave...

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第43届中国控制会议

作者： Xisheng Zhang Liheng Wang Na Shao Wuhan Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the error of DV-Hop positioning algorithm in wireless sensor networks,the positioning steps of this algorithm are explained and the reasons for the error are analyzed,and a DV-Hop positioning algorithm optimized based on the sparrow search algorithm is *** this paper,we first use the minimum mean square error criterion to correct the average jump distance,and then introduce the sparrow search algorithm into the solution process of unknown node *** results of the simulation experimental show that the positioning accuracy of the DV-Hop algorithm optimized by the sparrow search algorithm is greatly improved compared with the basic DV-Hop algorithm.

关键词： wireless sensor network DV-Hop sparrow search algorithm

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Research on multi-strategy improved sparrow search optimization algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第9期20卷 17220-17241页

作者： Fei, Teng Wang, Hongjun Liu, Lanxue Zhang, Liyi Wu, Kangle Guo, Jianing Tianjin Univ Commerce Inst Informat Engn Tianjin Peoples R China

To address the issues with inadequate search space, sluggish convergence and easy fall into local optimality during iteration of the sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA), is developed. First, the population dynamic adjustment strategy is carried out to restrict the amount of sparrow population discoverers and joiners. Second, the update strategy in the mining phase of the honeypot optimization algorithm (HBA) is combined to change the update formula of the joiner's position to enhance the global exploration ability of the algorithm. Finally, the optimal position of population discoverers is perturbed using the perturbation operator and levy flight strategy to improve the ability of the algorithm to jump out of local optimum. The experimental simulations are put up against the basic sparrow search algorithm and the other four swarm intelligence (SI) algorithms in 13 benchmark test functions, and the Wilcoxon rank sum test is used to determine whether the algorithm is significantly different from the other algorithms. The results show that the improved sparrow search algorithm has better convergence and solution accuracy, and the global optimization ability is greatly improved. When the proposed algorithm is used in pilot optimization in channel estimation, the bit error rate is greatly improved, which shows the superiority of the proposed algorithm in engineering application.

关键词： sparrow search algorithm population dynamic adjustment honeypot optimization algorithm perturbation operator pilot optimization

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Accelerometer-based human fall detection using sparrow search algorithm and back propagation neural network

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MEASUREMENT 2022年 204卷

作者： Wang, Tianhu Wang, Baoqiang Shen, Yunzhe Zhao, Yang Li, Wenjie Yao, Keming Liu, Xiaojie Luo, Yinsheng Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Jiangsu Peoples R China

To reduce the injury caused by the fall and solve the problems of low efficiency and low accuracy of traditional fall prediction methods, an optimized BP neural network fall prediction model based on the sparrow search algorithm (SSA) is established. Taking sliding window to extract discrete features, selecting the maximum value, minimum value, mean value, and variance as the output indicator, the three-axis acceleration and resultant acceleration as the input of influencing factors, the fall prediction model is established for error prediction after data preprocessing. Experimental results show that the improved BP neural network could avoid falling into the locally optimal solution, and the convergence speed is faster, the fall detection accuracy of 98.3%, 92.0% and 96.1% based on DLR, Smart Fall and URFall datasets, respectively. This study may provide technical supports for wearable fall detection that can adapt to different environmental requirements, portable and low power consumption.

关键词： Fall detection Aging sparrow search algorithm BP neural network

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Fast stochastic configuration network based on an improved sparrow search algorithm for fire flame recognition

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KNOWLEDGE-BASED SYSTEMS 2022年 245卷 1页

作者： Wu, Hao Zhang, Aihua Han, Ying Nan, Juan Li, Kun Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Liaoning Peoples R China Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China China Coal Technol & Engn Grp State Key Lab Coal Mine Safety Technol Shenyang Res Inst Shenfu Demonstrat Zone Fushun 113122 Liaoning Peoples R China

Flame image recognition is of great significance in the fire detection and prevention. In this paper, in order to improve the accuracy of fire recognition, a fast stochastic configuration network (FSCN) method based on an improved sparrow search algorithm (ISSA) is proposed. In the design of fast stochastic configuration network (FSCN), the gradual increase of hidden layer nodes in the original stochastic configuration network (SCN) is canceled, and the number of them is set directly. An improved sparrow search algorithm (ISSA) is used to generate the input weights and biases of hidden layer nodes. At the same time, the supervisory mechanism is retained to judge the weights and biases of all hidden layer nodes, and ISSA is used to regenerate corresponding weights and biases for the nodes that do not meet the constraints in the supervisory mechanism. In the ISSA, sine map, adaptive adjustment of hyper-parameters and mutation strategy are used to improve the optimization ability of the original sparrow search algorithm (SSA). Some parameters in FSCN are optimized by ISSA to make it have better classification performance. Finally, the image processing technology is used to extract features from the flame images and the interference images, and then the feature vectors are obtained to train the ISSA-FSCN. Several simulation experiments have been carried out to verify effectiveness of the proposed ISSA-FSCN method. In the performance verification of ISSA on CEC test suit, ISSA averagely outperforms other algorithms by 33.6% in the average results of 20 functions. In the performance verification of FSCN, the average results of accuracy, precision, recall, F-1 and auc are compared. In the experiment 1, ISSA-FSCN averagely outperforms other algorithms by 19.7%, 14.7%, 12.8%, 14.5% and 23.0%. In the experiment 2, ISSA-FSCN averagely outperforms other algorithms by 2.3%, 2.1%, 2.5%, 6.0%, 3.2%. In the experiment 3, ISSA-FSCN averagely outperforms other algorithms by 5.9%, 4.0

关键词： Stochastic configuration network sparrow search algorithm Sine map Fire flame recognition Color space Optimization

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A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a sparrow search algorithm

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SENSORS 2022年第22期22卷 8977-8977页

作者： Zhang, Haitao Han, Yaozhen Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan 250357 Peoples R China

To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and gas-concentration-prediction method based on a support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Principal component analysis (PCA) is applied to perform data dimensionality reduction on the input data, and SSA is adopted to optimize the SVM hyperparameters to improve the recognition rate and gas-concentration-prediction accuracy of mixed gases. For the mixed-gas identification, the classification accuracy is significantly improved under the proposed SSA optimization SVM method (SSA-SVM), compared with random forest (RF), extreme-learning machine (ELM), and BP neural network methods. With respect to gas-concentration prediction, the maximum fitting degrees reached 99.34% for single gas-concentration prediction and 97.55% for mixed-gas-concentration prediction. The experimental results show that the SSA-SVM method had a high recognition rate and high concentration-prediction accuracy in gas-mixture detection.

关键词： mixed-gas detection support vector machine sparrow search algorithm prediction classification

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New method for diagnosing resilience of agricultural soil-water resource composite system: Projection pursuit model modified by sparrow search algorithm

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JOURNAL OF HYDROLOGY 2022年 610卷

作者： Xu, Dan Liu, Deping Liu, Dong Fu, Qiang Huang, Yan Li, Mo Li, Tianxiao Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Effect Utilizat Agr Water Resources Minist Agr Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Heilongjiang Prov Key Lab Water Resources & Water Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Water Saving Agr Ordinary Univ Heilongjia Harbin 150030 Heilongjiang Peoples R China Heilongjiang Prov Hydraul Res Inst Harbin 150080 Heilongjiang Peoples R China

Considering the driving mechanism of agricultural soil-water resource composite system (ASWRS) resilience, the Jiansanjiang Branch of China Beidahuang Agricultural Reclamation Group Co., Ltd., is selected as the research object. A projection pursuit (PP) model improved by the sparrow search algorithm (SSA) was proposed to measure the key drivers of resilience, and the driving mechanism of resilience was analysed in combination with the logarithmic mean Divisia index (LMDI). The performance of the built model and the rationality of the results were verified, thus providing a new horizon for the healthy and sustainable development of agricultural water and soil resources and the environment. The results indicated that the SSA-PP model constructed in this paper was superior to PP models based on genetic algorithm (GA) optimization (GA-PP) and particle swarm optimization (PSO-PP) in regard to the solution speed and optimization ability, and these three models yielded similar conclusions. Compared with those of the entropy weight (EW) method, the driving results of the SSA-PP model are more significant, and the screened key driving factors are more reasonable. It was finally determined that the impact on the resilience of the ASWRS was the result of the joint action of economic and agricultural systems. The key driving factors included the per capita gross domestic product (GDP), paddy field proportion, per capita net income and grain output per unit area. According to the LMDI results, the economic level, grain output and paddy field proportion increased, which played a positive role in driving resilience. With the use of the autoregressive integrated moving average (ARIMA) model to simulate the future evolution of the key driving factors, it was predicted that the resilience level will increase year by year in the future. It was found that accelerating the economic development process, increasing the grain output, implementing rational planning and adjusting the agricu

关键词： Resilience Agriculturalsoil-water resource composite system sparrow search algorithm Projection pursuit Driving mechanism Logarithmic mean Divisia index

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Malicious URL Classification Model Based on Improved sparrow search algorithm 11

Malicious URL Classification Model Based on Improved Sparrow...

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11th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Ma, Yiran Guan, Qihang Guo, Fengyuan Zhang, Guidong Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9780738111353

With the development of the Internet, malicious URL attacks have shown a complex and changeable situation, and have always been one of the main factors that threaten network security. In order to detect malicious URLs more effectively, a random walk strategy is proposed to improve the sparrow search algorithm, which is compared and analyzed with the sparrow search algorithm, the gray wolf optimization algorithm and the whale optimization algorithm. After a benchmark test function experiment, it is verified that the improved sparrow search algorithm has the characteristics of fast convergence speed and high optimization precision. In order to further verify the practicability of the improved algorithm, the optimal solution of the random walk strategy optimization sparrow search algorithm is used as the initial neuron connection weight and threshold information of the BP neural network and applied to the classification of malicious URLs. The results show that the random walk strategy optimization sparrow search algorithm has a certain ability to optimize the BP network, which can improve the classification accuracy of malicious URLs.

关键词： component random walk strategy sparrow search algorithm BP neural network malicious URL classification

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