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Multi-objective modeling and optimization of water distribution for canal system considering irrigation coverage in artesian irrigation district

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AGRICULTURAL WATER MANAGEMENT 2024年 301卷

作者： Mai, Zijun He, Yupu Feng, Chen Han, Congying Shi, Yuanzhi Qi, Wei Nanjing Hydraul Res Inst Nanjing 210029 Peoples R China State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Sch Elect & Power Engn Nanjing 211100 Peoples R China Minist Water Resources China Inst Water Resources & Hydropower Res Key Lab Digital Twin watershed State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China

Efficient water distribution within canal systems is essential for conserving water resources and enhancing irrigation efficiency, especially under conditions of constrained water supply. This study proposed an innovative optimization model for canal systems, incorporating the artesian irrigation rate as a multi-objective criterion. A digital elevation model was utilized to determine the irrigation coverage capacity based on outlet water levels, addressing existing limitations by introducing the artesian irrigation rate objective. A multi-objective optimization model was constructed, taking into account drainage loss, fluctuation of trunk canal flow, and artesian irrigation rate within irrigated areas. NSGAIII, with an effective correction strategy, and entropy weight method were used to solve the optimization problem under restricted flow constraints. The results indicate that the model's implementation has reduced the overall water conveyance time by 3 hours, decreased leakage losses by 1.3 %, elevated the water level of the branch canals, and increased the artesian irrigation rate by 4.5 %. The proposed objective enhances the flows within the branch canals and significantly ameliorates the rate of artesian irrigation under various flow limitation conditions.

关键词： Artesian irrigation district Water distribution optimization Multi-objective modeling Canal flow restriction intelligent optimization algorithm

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Cosine-enhanced tuna swarm optimized exponential entropy segmentation method for sand grain microscopic images

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COMPUTERS & GEOSCIENCES 2024年 190卷

作者： Wang, Mengfei Wang, Weixing Zheng, Richeng Li, Limin Li, Hongxia Yan, Di Khatoon, Amna Wenzhou Univ Sch Elect & Elect Engn Wenzhou 325035 Zhejiang Peoples R China Changan Univ Sch Informat Xian 710064 Shaanxi Peoples R China North China Univ Sci & Technol Sch Elect Engn Tangshan 063210 Hebei Peoples R China

The feature extraction of sand grain size, color and texture is a necessary step to identify clastic components. The sand grain features are complex and various, which brings difficulties to geological identification. Aiming at these images, a cosine-enhanced tuna swarm optimized exponential entropy segmentation method is proposed, which can effectively preserve the texture features of various sand grains. Firstly, for the tuna swarm optimization (TSO) algorithm, three improvement strategies are proposed: cosine spiral movement, cosine parabolic movement and Gauss-Cauchy mutation, which improve the TSO's global and local search. This algorithm is called cosine-enhanced TSO (CETSO). Benchmark function experiments showed that the convergence accuracy and stability of CETSO are greatly improved, and the convergence speed is also slightly increased. Secondly, CETSO optimized the exponential entropy to automatically determine the segmentation thresholds, and the feasibility of the method is verified by taking the information content of the segmented image as the standard. Finally, segmentation experiments were carried out on the Yarlung Zangbo River sand microscopic image dataset, and the results show that the method has high segmentation accuracy and stability for images with high contrast, rich texture, or significant differences in the size of sand debris. Compared with TSO, the CETSO optimized exponential entropy segmented images achieved an improvement of 21% and 93% in the evaluation of the average and standard deviation of the peak signal-to-noise ratio on thirty experiments. And this method has a fast processing speed, and it only takes about 0.85s to divide an image on average, which meets the needs of engineering applications.

关键词： Sand grain microscopic image Multilevel image segmentation intelligent optimization algorithm Exponential entropy Tuna swarm optimization

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Research on Distributed Multi-Sensor Cooperative Scheduling Model Based on Partially Observable Markov Decision Process

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SENSORS 2022年第8期22卷 3001页

作者： Zhang, Zhen Wu, Jianfeng Zhao, Yan Luo, Ruining Air Force Engn Univ Air Def & Missile Def Coll Xian 710051 Peoples R China

In the context of distributed defense, multi-sensor networks are required to be able to carry out reasonable planning and scheduling to achieve the purpose of continuous, accurate and rapid target detection. In this paper, a multi-sensor cooperative scheduling model based on the partially observable Markov decision process is proposed. By studying the partially observable Markov decision process and the posterior Cramer-Rao lower bound, a multi-sensor cooperative scheduling model and optimization objective function were established. The improvement of the particle filter algorithm by the beetle swarm optimization algorithm was studied to improve the tracking accuracy of the particle filter. Finally, the improved elephant herding optimization algorithm was used as the solution algorithm of the scheduling scheme, which further improved the algorithm performance of the solution model. The simulation results showed that the model could solve the distributed multi-sensor cooperative scheduling problem well, had higher solution performance than other algorithms, and met the real-time requirements.

关键词： distributed defense multi-sensor scheduling partially observable Markov decision process intelligent optimization algorithm

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Interior sound quality evaluation and forecasting of passenger vehicles based on hybrid optimization neural networks

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JOURNAL OF VIBRATION AND CONTROL 2024年

作者： Qian, Kun Tan, Jing Shen, Zhenghua Liu, Ke Wang, Yanfu Duan, Jiying Du, Xikang Zhao, Jian Dalian Univ Technol Sch Mech Engn Dalian Peoples R China

The interior noise of vehicles directly affects the comfort of the occupants, necessitating precise evaluation and control. Existing research has focused on constructing mappings between objective parameters and subjective perceptions of noise, where back propagation neural networks (BPNNs) are widely used due to their strong nonlinear mapping capabilities. However, the selection of initial weights and thresholds can affect the predictive accuracy of BPNN. This study developed a BPNN model optimized by an intelligent algorithm for predicting the level of subjective annoyance of passengers during the movement. Initially, objective parameters of interior noise were obtained through acoustic signal processing techniques, and five parameters were selected for studying subjective annoyance through correlation analysis and two-tailed tests. Meanwhile, the actual subjective ratings of passengers on interior noise were captured for subsequent training of the model and testing of the results. Finally, the established sparrow search algorithm (SSA) and genetic algorithm (GA) optimized BPNN were used to predict subjective evaluations. The predictive accuracy and efficiency of the model were significantly improved upon validation, providing a viable alternative to traditional passenger vehicle noise assessment experiments and valuable references for future noise control and optimization efforts. The experimental results are consistent with the view that the neural network model optimized with a mixture of intelligent algorithms is closer to the passenger's subjective annoyance level having higher accuracy and efficiency.

关键词： Vehicle interior noise sound quality annoyance evaluation artificial neural network intelligent optimization algorithm

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Prediction and design of high hardness high entropy alloy through machine learning

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MATERIALS & DESIGN 2023年 235卷

作者： Ren, Wei Zhang, Yi-Fan Wang, Wei-Li Ding, Shu-Jian Li, Nan Northwestern Polytech Univ Xian 710072 Peoples R China Xian Univ Posts & Telecommun Sch Sci Xian 710121 Peoples R China

Two data-driven machine learning (ML) models were proposed for the hardness prediction of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs, respectively. The hardness prediction model combined interpretable ML methods with solid solution strengthening theory, and the R2 and RMSE values of 0.9716 and 39.2525 were respectively achieved under the leave-one-out validation method. The optimization model adopted an intelligent optimization algorithm to design the optimized elemental molar ratios of high hardness HEAs and was experimentally verified. A general design framework was summarized for prediction and composition optimization of various HEA performances.

关键词： High-entropy alloy Machine learning intelligent optimization algorithm Interpretable machine learning Solid solution strengthening

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FPGA-Based Speed Control Strategy of PMSM Using Improved Beetle Antennae Search algorithm

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ENERGIES 2024年第8期17卷 1870页

作者： Wu, Caiyun Zhang, Kai Zhang, Xin Shenyang Ligong Univ Sch Equipment Engn Shenyang 110159 Peoples R China Shenyang Ligong Univ Sch Automot & Transportat Shenyang 110159 Peoples R China

To improve performance in terms of overshoot and motor response speed when a permanent-magnet synchronous motor (PMSM) with a proportional-integral (PI) controller is subjected to external disturbances, this paper proposes a speed control strategy based on an enhanced Beetle Antennae Search algorithm, which allows for adjustable parameters of the PI controller within a certain range. Firstly, to enhance the global and local search capabilities of each individual beetle, the step size was improved by linearly decreasing it. Secondly, a simulation model of a PMSM closed-loop control system was built to verify the effectiveness of the improved Beetle Antennae Search (BAS) algorithm. Finally, a linear feedback shift register model that generates four random numbers was developed on a field-programmable gate array (FPGA). The improved BAS algorithm for the PMSM control system was implemented on an FPGA using the Verilog hardware description language, and the feasibility of the system was verified through hardware simulation. Additionally, the hardware resource consumption on different FPGA platforms was analyzed. The simulation results demonstrate that the proposed new speed control strategy can reduce the overshoot and improve the motor response speed.

关键词： PMSM control system beetle antennae search field-programmable gate array intelligent optimization algorithm

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A Method for Optimizing Terminal Sliding Mode Controller Parameters Based on a Multi-Strategy Improved Crayfish algorithm

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APPLIED SCIENCES-BASEL 2024年第17期14卷 8085页

作者： Wei, Zhenghao He, Zhibin Yang, Fumiao Sun, Bin Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China

This paper proposes a parameter optimization method for a terminal sliding mode controller (TSMC) based on a multi-strategy improved crayfish algorithm (JLSCOA) to enhance the performance of ship dynamic positioning systems. The TSMC is designed for the "Xinhongzhuan" vessel of Dalian Maritime University. JLSCOA integrates subtractive averaging, Levy Flight, and sparrow search strategies to overcome the limitations of traditional crayfish algorithms. Compared to COA, WOA, and SSA algorithms, JLSCOA demonstrates superior optimization accuracy, convergence performance, and stability across 12 benchmark test functions. It achieves the optimal value in 83% of cases, outperforms the average in 83% of cases, and exhibits stronger robustness in 75% of cases. Simulations show that applying JLSCOA to TSMC parameter optimization significantly outperforms traditional non-optimized controllers, reducing the average time for three degrees of freedom position changes by over 300 s and nearly eliminating control force and velocity oscillations.

关键词： terminal sliding mode controller ship dynamic positioning parameter optimization crayfish algorithm multi-strategy integration intelligent optimization algorithm

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Tower Crane Layout Planning: Multi-Optimal Solutions algorithm

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BUILDINGS 2024年第12期14卷 3760-3760页

作者： Huang, Xiaokang Chen, Huazhou Wang, Rui Wang, Heng Li, Shuai Zhang, Pengfei Yang, Zhen China Construct Sci & Ind Corp Ltd Shenzhen 518000 Peoples R China Taiyuan Univ Technol Coll Civil Engn Taiyuan 030024 Peoples R China China Construct Sci & Ind Corp Tianjin Ltd Tianjin 300000 Peoples R China

Effective tower crane layout planning is essential for the success of construction projects. Traditional optimization algorithms, which often provide a single optimal solution, may not always reveal the global optimum, leaving room for doubt. This paper introduces the competitor algorithm, a novel multi-optimal solution approach inspired by the competitive learning paradigm within classroom settings. This algorithm is designed to provide users with a diverse set of competitive solutions, while avoiding falling into local optima. This strategic diversification ensures that users are equipped with a comprehensive range of options, empowering them to make confident, informed decisions. Furthermore, we have streamlined the positioning range for tower cranes, transitioning from a two-dimensional plane to a one-dimensional segmented line, thus eliminating the need to explore extensive, non-competitive regions. The competitor algorithm's performance was validated through practical application, showcasing both its stability and optimization prowess, thereby confirming its reliable utility in real-world scenarios.

关键词： competitor algorithm multiple optimal solutions intelligent optimization algorithm tower crane layout planning tower crane positioning range

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A stacking-based ensemble learning method for earthquake casualty prediction

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APPLIED SOFT COMPUTING 2021年 101卷 107038-107038页

作者： Cui, Shaoze Yin, Yunqiang Wang, Dujuan Li, Zhiwu Wang, Yanzhang Dalian Univ Technol Sch Econ & Management Dalian 116023 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China

The estimation of the loss and prediction of the casualties in earthquake-stricken areas are vital for making rapid and accurate decisions during rescue efforts. The number of casualties is determined by various factors, necessitating a comprehensive system for earthquake-casualty prediction. To obtain accurate prediction results, an effective prediction method based on stacking ensemble learning and improved swarm intelligence algorithm is proposed in this study, which comprises three parts: (1) applying multiple base learners for training, (2) using a stacking strategy to integrate the results generated by multiple base learners to obtain the final prediction results, and (3) developing an improved swarm intelligence algorithm to optimize the key parameters in the prediction model. To verify the effectiveness of the model, we collected data pertaining to earthquake destruction from 1966 to 2017 in China. Experiments were conducted to compare the proposed method with popular machine learning methods. It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy. Moreover, the importance of each feature was evaluated, which has important implications for future work such as casualty prevention and rescue during earthquakes. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Earthquake Casualty prediction Ensemble learning Stacking intelligent optimization algorithm

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A Hybrid Multivariate Multistep Wind-Speed Forecasting Model Based on a Deep-Learning Neural Network

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JOURNAL OF ENERGY ENGINEERING 2024年第6期150卷 04024035-04024035页

作者： Wei, Donglai Tian, Zhongda Shenyang Univ Technol Sch Informat Sci & Engn 111 Shen Liaoxi RdShenyang Econ & Technol Dev Zon Shenyang 110870 Peoples R China Shenyang Univ Technol Sch Artificial Intelligence 111 Shen Liaoxi RdShenyang Econ & Technol Dev Zon Shenyang 110870 Peoples R China

Predicting wind speed is a complex undertaking influenced not only by the wind-speed sequence itself but also by various meteorological factors. This paper introduces a novel multivariate deep-learning neural network prediction model that takes into account not only historical wind-speed data but also a series of meteorological features relevant to wind speed. The meteorological features associated with wind speed are initially extracted using the random forest algorithm (RF). Subsequently, Variational Mode Decomposition and Autocorrelation Function analysis are employed for noise reduction in the wind-speed series. Finally, the wind-speed series are predicted using a Gated Recurrent Unit (GRU) deep-learning neural network, and an Improved Sparrow Search algorithm is proposed to optimize the four parameters of the GRU. To validate the predictive performance of the model, experimental data from three cities in China, Shenyang, Dalian, and Yingkou, are utilized. The experimental results demonstrate that our proposed model outperforms other models, as evidenced by four key performance indicators.

关键词： Wind-speed forecasting intelligent optimization algorithm Deep-learning Multivariate time series forecasting

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