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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Sheng, Xinxin Yang, Qingbo Ye, Ruili Xie, Lin Feng, Chaohan Tao, Lei Zhang, Zhoujie China Electric Power Research Institute Beijing Key Lab of Research and System Evaluation of Power Dispatching Automation Technology Beijing China

ISBN: (纸本)9798350363586

The energy storage system can effectively solve the challenges brought by the high proportion of renewable energy access to the power grid. In this paper, a big data feature mining method based on double-layer intelligent optimization algorithm is proposed. Considering the differences in operating conditions of energy storage systems in photovoltaic power stations caused by different seasonal scenarios and different weather scenarios, the improved bird swarm algorithm is proposed to mine the big data features of energy storage systems in photovoltaic power stations under typical weather scenarios. The inherent operation law of the energy storage system in the actual photovoltaic power station is revealed, and the charging and discharging characteristics of the actual energy storage system are reflected. © 2024 IEEE.

关键词： big data energy storage system feature mining intelligent optimization algorithm photovoltaic power station the dispatching and control cloud

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WSN node location based on beetle antennae search to improve the gray wolf algorithm

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WIRELESS NETWORKS 2022年第2期28卷 539-549页

作者： Yu, Xiu-wu Huang, Lu-ping Liu, Yong Zhang, Ke Li, Pei Li, Ying Univ South China Hengyang Peoples R China Hunan Engn Res Ctr Uranium Tailings Decommiss & T Hengyang Peoples R China Wuhan Univ Technol Wuhan Peoples R China

With the rapid development of the Internet, more and more people pay attention to wireless sensor networks. Localization technology plays a vital role in wireless sensor networks. To reduce the localization error and improve the localization stability, a gray wolf localization algorithm based on beetle antennae search (BASGWO) is proposed, transforming the node localization problem into function constrained optimization. Firstly, the excellent point set method is used to initialize the gray wolf population, improving the richness. Secondly, the beetle antennae search mechanism with good global search ability is introduced into the gray wolf algorithm to avoid the gray wolf algorithm falling into local optimization in the late iteration. The gray wolf is the beetle antennae in search of excellence. The location of the gray wolf was updated according to the fitness value of the gray wolf and beetle antennae. The optimal global solution can be obtained, and then the unknown node coordinates can be obtained. The improved gray wolf algorithm improves the localization accuracy by 24% through simulation comparison and reduces the localization error fluctuation by 23%. Compared with the classical localization algorithm of WSN, the solution ability and localization accuracy of the BASGWO algorithm are improved.

关键词： Wireless sensor network Beetle antennae search Gray wolf algorithm Node positioning Good point set intelligent optimization algorithm Positioning accuracy

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Endmember Bundle Extraction Based on Multimodal and Multiobjective Quantum Particle Swarm Optimizer and Relative Spectral Angle Distance

Endmember Bundle Extraction Based on Multimodal and Multiobj...

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2024 International Conference on Guidance, Navigation and Control

作者： Lin, Jiewen Chen, Jian Mao, Xing Ren, Ni China Agr Univ Coll Engn Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Trop South China Guangzhou 510642 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Yangtze River Delta Nanjing 210044 Peoples R China

ISBN: (纸本)9789819622269;9789819622245;9789819622238

To effectively avoid the impact of spectral variability in hyperspectral images on endmember extraction, this paper proposes a method for endmember bundle extraction utilizing a multimodal and multiobjective quantum particle swarm optimizer and relative spectral angle distance (MMQPSORSAD). Firstly, the particles are encoded based on rows and columns, and their positions are updated through quantum particle swarm optimization. A relative spectral angle distance method was proposed to calculate the crowding distance based on the consistency of spectral curves of objects on the same site and then combined with the target space for comprehensive sorting, to achieve the extraction of endmember bundles. When the number of particles and the iterations are 20 and 300, respectively. The root mean square error (RMSE) of the algorithm on the Samson dataset and MUUFL dataset are 0.0058 and 0.0091, respectively. This provides an effective method for extracting endmember bundles using intelligent optimization algorithms in multimodal and multiobjective situations.

关键词： Hyperspectral image Spectral unmixing Endmember intelligent optimization algorithm

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A Clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations

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系统仿真学报 2024年第12期36卷 2782-2796页

作者： Zhou Yaqiong Chen Junqi Li Weishi Qiu Sihang Ju Rusheng College of System Engineering National University of Defense TechnologyChangsha 410073China

To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic *** establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal *** on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun *** results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.

关键词： location problem clustering algorithm intelligent optimization algorithm Pareto front

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Multi-modal and multi-objective hyperspectral unmixing model based on multi-source data

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 227卷

作者： Lin, Jiewen Chen, Jian China Agr Univ Coll Engn Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Yangtze River Delta Nanjing 210044 Peoples R China

Mixed pixels in hyperspectral images are a common imagination in the process of hyperspectral image processing, usually processed using mixed pixel decomposition. To explore the effect of multi-source data fusion on endmember bundle extraction, a multi-modal and multi-objective optimization endmember bundle extraction method based on data fusion (MMO-CDPSO-RSADRDSM) is proposed. Firstly, the initialization particles are randomly assigned in the region by DSM interference;Secondly, according to the characteristics of hyperspectral image space and DSM space, the crowding distance of decision space is improved to improve the diversity of decision space;Finally, the DSM derived weights are integrated into the optimization model to calculate the objective function, and the endmember bundle is extracted iteratively by the above method. The effective endmember bundles extracted by the MMO-CDPSO-RSADRDSM algorithm have mean RMSE (mRMSE) of 0.1853 and 0.1548 on MUUFL and Houston data, respectively, and recombinant minimum mSAD (re-min mSAD) of 0.0325 and 0.0341. The number of endmemebers extracted on the MUUFL dataset is 35, and in Houston it is 36. The experimental results show that this method has good recognition ability for endmembers with high differences and similar hyperspectral bands, and extracts more effective endmember bundles on this basis. This provides a new method for extracting endmember bundles for multi-source data fusion.

关键词： Hyperspectral image Endmember extraction Data fusion intelligent optimization algorithm Multi-modal and Multi-objective

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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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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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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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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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