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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective *** traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical *** recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)*** this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)*** presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the *** attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic *** order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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Study on Intelligent Optimization Model of Porous Hydraulic Buffer

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INTERNATIONAL JOURNAL OF FLUID POWER 2022年第3期23卷 253-270页

作者： Zheng, Yangbing Xue, Xiao Zhang, Jisong Nanyang Normal Univ Coll Mech & Elect Engn Nanyang 473061 Henan Peoples R China Qinghai Wandong Ecol Environm Dev Co LTD Geermu 816000 Qinghai Peoples R China Nanyang Inst Technol Sch Informat Engn Nanyang 473004 Henan Peoples R China

In order to improve the buffering performance of porous hydraulic buffer effectively, the artificial fish swarm algorithm is improved to carry out optimization analysis of porous hydraulic buffer. Firstly, theory model of porous hydraulic buffer is studied, and the optimal model of porous hydraulic buffer is established. Secondly, the improved artificial fish swarm algorithm is put forward through improving step size and fish swarm behaviour. Finally, the optimization simulation analysis of porous hydraulic buffer is carried out, and results show that the proposed artificial fish swarm algorithm can obtain better optimal effect, and improve the performance of porous hydraulic buffer.

关键词： Optimization porous hydraulic buffer artificial fish swarm algorithm

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A logistics distribution route optimization model based on hybrid intelligent algorithm and its application

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ANNALS OF OPERATIONS RESEARCH 2022年 1-13页

作者： Gan, Quan Philippine Christian Univ Ctr Int Educ Manila 1004 Philippines Chaohu Univ Sch Business Adm Chaohu 238024 Anhui Peoples R China

Logistics distribution is the link that directly connects consumers in logistics activities. Among various logistics costs, distribution costs account for a large proportion. Reasonable vehicle routes in distribution have a significant impact on the service level, cost and benefit of logistics distribution. Optimizing vehicle routes using scientific and reasonable methods is an important research topic in the field of logistics distribution. Among them, the problem of optimizing the route of delivery vehicles with a time frame is the focus of current research. The latest intelligent optimization algorithms include tabu search algorithm, simulated annealing algorithm, ant colony algorithm and artificial fish swarm algorithm. The emergence of these algorithms provides new tools for solving the optimal transportation problem of delivery vehicles. The calculation of the vehicle allocation route problem using the time frame is more complicated and belongs to the NP-hard problem. This paper studies the construction of a vehicle allocation route problem model with a time frame. Based on fish swarm algorithm and ant colony algorithm, a hybrid optimization algorithm is proposed for the distribution route optimization problem. Aiming at the shortcomings of ant colony algorithm, we use artificial fish swarm algorithm to obtain the initial solution, select the pheromone update strategy, and improve the probability of state transition. At the same time, the ant colony algorithm is improved. The congestion concept in the fish school algorithm of the ant colony algorithm ensures that most ants are not affected by the pheromone concentration. Random optimization is carried out in the early stage of the optimization process, which increases. Aiming at the problem that the ant colony algorithm takes a long time to solve and tends to stagnation, the concept of transfer coefficient is applied to improve the calculation speed of the ant colony, and enhance the scanning ability and optim

关键词： Logistics distribution Vehicle routing optimization Ant colony algorithm artificial fish swarm algorithm Hybrid swarm intelligence algorithm

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Optimization of Rolling Schedules Based on Improved artificial fish swarm algorithm for Aluminum Strip Hot Rolling Mills

Optimization of Rolling Schedules Based on Improved Artifici...

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32nd Chinese Control Conference (CCC)

作者： Yang Jing-ming Xu-Shuang Che Haijun Zhao Zhiwei Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9781479900305

The quality and yield of the products in the process of hot rolling is affected seriously by slipping phenomenon. In order to solve the phenomenon in aluminum hot rolling, on the basis of considering various factors which lead to slip, the concept of hot rolling slip factor is proposed, and a multi-objective function with equal power margin and preventing the slip phenomenon is established, and the improved artificial fish swarm algorithm is applied to optimize the reduction of first three stands. Adaptive step and improved move strategy are introduced into the proposed algorithm on the basis of basic fish swarm algorithm, which has better accuracy of optimization and faster convergence speed. The optimized schedule is applied to roll aluminum of 1050 in one hot rolling mill, and the performance is satisfied.

关键词： aluminum hot tandem rolling hot rolling slip factor rolling schedule artificial fish swarm algorithm

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Dynamic Reactive Power Characteristics Analysis and Optimization of Synchronous Condensers 10

Dynamic Reactive Power Characteristics Analysis and Optimiza...

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10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Xu, Yi Pan, Xueping Guo, Jinpeng Sun, Xiaorong Zhang, Haiyan Hohai University College of Energy and Electrical Engineering Nanjing China

ISBN: (纸本)9798350309379

In the current context of widespread adoption of new energy sources and the prevalence of weak AC power systems in the grid, it is of significant importance to investigate the dynamic response characteristics and fully exploit the reactive power potential of synchronous condensers, which are essential reactive power compensation devices addressing voltage stability. In this paper, a mathematical model is firstly built for synchronous condensers, based on which the effective reactive current gain is introduced as a performance metric. The dominant motor and excitation parameters of synchronous condensers along with their optimization directions are then determined by applying the frequency-domain sensitivity analysis. Using the average effective reactive current gain as the objective function, a joint optimization model for motor and excitation parameters is established, and it is solved using the artificial fish swarm (AFS) algorithm. Taking a 4-machine 2-area system as an example, the effectiveness of the joint optimization approach is validated. The simulation results demonstrate that the optimized synchronous condenser exhibits superior steady-state and transient reactive power support capabilities, thus confirming the feasibility of the parameter optimization method proposed in this paper. © 2023 IEEE.

关键词： artificial fish swarm algorithm distributed synchronous condenser dynamic reactive power characteristics parameter optimization reactive current gain

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Thermal Zero Drift Compensation of Pressure Sensor Based on Data Mining and BP Neural Network 5th

Thermal Zero Drift Compensation of Pressure Sensor Based on ...

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5th EAI International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Li, Ya-ping Zhao, Dan Beijing Polytech Coll Mechatron Engn Beijing 100176 Peoples R China

ISBN: (纸本)9783030945510;9783030945503

Due to the poor compensation accuracy, the traditional compensation algorithm for thermal zero shift of pressure sensor results in large error of pressure measurement. Therefore, this paper proposes a pressure sensor thermal zero drift compensation algorithm based on data mining and BP neural network. Combined with the data mining process, the characteristics of the thermal zero drift of the pressure sensor are analyzed, and the hysteresis and nonlinear characteristic curve of the pressure sensor is obtained to prepare for the compensation of the thermal zero drift. Then BP neural network is introduced to determine the parameter update mode, which is effectively combined with artificial fish swarm algorithm, and the compensation of pressure sensor thermal zero shift is realized by implementing the thermal zero shift compensation algorithm of pressure sensor. The experimental results show that the pressure measurement error range of the algorithm in this paper is 0.30 N-1.45 N. Compared with the three existing algorithms, the pressure measurement error of the algorithm in this paper is smaller, which indirectly shows that the algorithm in this paper has a higher thermal zero drift compensation accuracy, which fully shows that the algorithm in this paper compensates better performance.

关键词： Data mining BP neural network Pressure sensor drift compensation artificial fish swarm algorithm

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Adaptive feature extraction of underwater target signal based on mathematical morphology feature enhancement

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JOURNAL OF VIBRATION AND CONTROL 2024年

作者： Li, Zhaoxi Li, Yaan Zhang, Kai Xian Univ Posts & Telecommun Sch Digital Arts 618 West Changan Str Xian 710121 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL USA

This paper proposes an adaptive feature extraction method based on mathematical morphology enhancement to extract effective and stable features under strong ocean noise. Firstly, the traditional mathematical morphology method is improved and a new mathematical morphology filtering method is proposed for feature enhancement of underwater target signals. Secondly, the artificial fish swarm algorithm (AFSA) is improved using weighted power spectral kurtosis (WPSK) to achieve parameter adaptivity. Five measured underwater target signals are used to validate the extracted method, and cosine similarity (CS), signal to noise ratio (SNR), and refined composite multiscale fluctuation dispersion entropy (RCMFDE) are used as evaluation metrics to assess the results and verify the effectiveness of the proposed method. Compared with average filtering (AVGF) and closing opening filtering (COF), the proposed method shows better capability in adaptive feature extraction. Therefore, the proposed mathematical morphological filtering can enhance underwater target features and is very important for adaptive feature extraction of underwater targets.

关键词： Feature enhancement improved mathematical morphology filtering artificial fish swarm algorithm weighted power spectral kurtosis adaptive feature extraction underwater target signal

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Prediction of the Water Cut with the Hybrid Optimized SVR 12th

Prediction of the Water Cut with the Hybrid Optimized SVR

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12th International Conference on Applications and Technologies in Information Security (ATIS)

作者： Pan, Shaowei Mou, Yuhui Zheng, Zechen Xian Shiyou Univ Xian 710065 Shaanxi Peoples R China

ISBN: (纸本)9789811911668;9789811911651

The moisture content identification of a water drive oilfield is related to how to formulate and adjust the development plan of the oil field. However, traditional prediction methods for the water cut usually have problems such as slow recognition speed and limited by the specific conditions of the oil well. Therefore, in order to avoid the influence of the above problems, a machine algorithm model of water cut based on hybrid optimization is proposed, which is based on the support vector regression (SVR) model. First, the data is constructed by time sliding window;secondly, on the basis of the fundamental SVR model, this paper combines the Particle swarm Optimization (PSO) and the artificial fish swarm algorithm (AFSA) to optimize the hyperparameters of the SVR prediction model to achieve better experimental results;finally, if the hybrid model proposed in this article has some good experimental results, then it can be applied to the actual water cut prediction of the oilfield. After comparing four different models, the prediction model based on the hybrid optimization algorithm proposed in this paper has some good experimental results. The prediction curve and the real curve have the same trend as a whole, and the subtle errors are also the smallest. Thus, it performs better than the SVR prediction model optimized by the differential evolution algorithm, the SVR prediction model optimized by the genetic algorithm, and the SVR prediction model optimized by the PSO.

关键词： Water cut identification Particle swarm optimization algorithm artificial fish swarm algorithm SVR Prediction

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Human-Induced Vibration Control of Floor Structures Using MTMD System Optimized by MATLAB-SAP2000 Interface

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BUILDINGS 2024年第2期14卷 308-308页

作者： Zhang, Quanwu Shi, Weixing Wang, Yanze Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China

Under human-induced excitations, a floor structure may suffer excessive vibrations due to its large span and low damping ratio. Vertical vibrations, in particular, can become intolerable during resonance events. A tuned mass damper (TMD) is a widely used single-degree-of-freedom dynamic vibration absorber. To enhance the serviceability of a floor structure, a multiple TMD (MTMD) system finds broad application. The parameters of the MTMD must be carefully designed to achieve satisfactory performance. However, existing studies often employ a simplified model of the floor structure with closely spaced modes to optimize the parameters of MTMD. Nonetheless, an oversimplified floor model can lead to a reduction in its control effect. To solve this problem, this study utilizes the OAPI facility of SAP2000 to build a connection with MATLAB. A multi-objective optimization algorithm based on the artificial fish swarm algorithm (AFSA) for MTMD is developed in MATLAB, while the finite element model of a real floor structure is built in SAP2000. The locations of the MTMD system are initially specified in SAP2000 and, through the proposed MATLAB-SAP2000 interface, data can be exchanged between them. Based on the structural dynamic responses to external excitations in SAP2000, the optimization process for the MTMD is carried out in MATLAB. Concurrently, the parameters of the MTMD in SAP2000 are iteratively adjusted until they reach their final optimal values. To underscore the enhancements brought about by the proposed interface and optimization method, a comparative case study is conducted. A group of MTMDs, optimized using a conventional method, is presented for reference. The numerical results indicate that, overall, the proposed MTMD system exhibits superior control effectiveness and robustness.

关键词： human-induced vibration floor structure multiple tuned mass damper SAP2000-OAPI artificial fish swarm algorithm parameter optimization

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Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第5期34卷 e4746-e4746页

作者： Ramathilagam, Arunagiri Vijayalakshmi, Kandasamy PSR Engn Coll Dept Comp Sci & Engn Sivakasi 626140 India Ramco Inst Technol Dept Comp Sci & Engn Rajapalayam 626117 India

Workflow scheduling is the most focused research issue in the on-demand clouds where the user satisfaction like cost and bandwidth is more difficult. Several research works have been conducted earlier towards performing reliable workflow scheduling with the aim of reducing cost or execution time. However, those works lack to produce better result by compromising any attributes for attaining the goal. The existing work lacks from the security where the tasks might get corrupted during execution. To resolve this problem, an Enhanced artificial fish swarm algorithm (EAFSA)-based IaaS Cloud Partial Critical Path (IC-PCP) Replication and Hyper Elliptic Curve Cryptography (EAFSAIPR with HECC) is proposed. The main goal is to perform better workflow scheduling which can complete the task execution before deadline given by the users. This is done by predicting the early start time and latest finish time using EAFSA algorithm, so that task replication can be made to meet the soft deadline constraint. The task authentication is done efficiently using HEEC algorithm, so that corruption from malicious users can be avoided. The task replication is done securely using the cryptographic algorithm. The proposed EAFSAIPR with HECC algorithm uses idle time of provisioned resources to replicate workflow tasks optimally. The proposed EAFSAIPR algorithm scheduler focused to ensure the lowest cost while serving a deadline set by the user. The experimental results show that the scheduler can find good schedules of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

关键词： artificial fish swarm algorithm cloud computing scientific workflows soft deadline task replication

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