Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory by Tianyi Wang, Wenjun Yi, Youran Xia

“…The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. …”
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Low-Injury Rubber Tapping Robots: A Novel PSO-PID Approach for Adaptive Depth Control in <i>Hevea Brasiliensis</i> by Ruiwu Xu, Yulan Liao, Junxiao Liu, Zhifu Zhang, Xirui Zhang

“…Nevertheless, natural rubber tapping robots encounter considerable challenges in achieving precise tapping, particularly in controlling tapping depth, due to the lack of suitable control algorithms. To solve this problem, an improved Particle Swarm Optimization/Proportional–Integral–Derivative (PSO-PID) control method has been proposed in this paper. …”
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铰链四杆刚体导引机构综合的区间逃逸粒子群算法 by 易建, 何兵, 车林仙

“…The length of the bars is regarded as the restrict condition to obtain the unconstrained optimization model for rigid-body guidance approximate kinematc synthesis of hinged 4-bar linkages and this optimal problem is solved by means of the particle swarm optimization （PSO） algorithm. …”
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Multi-UAVs task allocation method based on MPSO-SA-DQN by Peng Pengfei, Gong Xue, Zheng Yalian

“…Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. …”
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Real-time active cell balancing using QPSO-controlled switched capacitor and transformer methods by S. Ida Evangeline, B. Subashini

“…To address these limitations, this paper proposes a novel hybrid active balancing approach that integrates switched capacitor and transformer-based techniques, dynamically controlled by a quantum particle swarm optimization algorithm. The hybrid system combines the speed of switched capacitor balancing for localized voltage differences with the long-range capabilities of transformer-based balancing, enabling efficient energy redistribution across large battery packs. …”
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Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule by Dingyuan Fan, Fei Yang, Jinghao Ji, Zexi Zhang

“…The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. …”
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An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes by Juan Lu, Xiaoping Liao, Steven Li, Haibin Ouyang, Kai Chen, Bing Huang

“…Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. …”
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Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults by Lijun Wang, Shengfei Ji, Nanyang Ji

“…Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM) algorithms, it is demonstrated that the SFLA-SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. …”
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Federated learning resource management for energy-constrained industrial IoT devices by Shaoshuai FAN, Jianbo WU, Hui TIAN

“…Given the impact of limited wireless resources, a dynamic multi-dimensional resource joint management algorithm was proposed, which intended to tackle the problem of device failure and training interruption caused by the limited battery energy in federated learning network in industrial Internet of things (IIoT).Firstly, the optimization problem was decoupled into battery energy allocation, equipment resource allocation and communication resource allocation sub-problems which were interdependent with the goal of maximizing the fixed-time learning accuracy.Then, the equipment transmission and computing resource allocation problem were solved based on particle swarm optimization algorithm under the given energy budget.Thereafter, the resource block iterative matching algorithm was proposed to optimize the optimal communication resource allocation strategy.Finally, the online energy allocation algorithm was proposed to adjust the energy budget allocation.Simulation results validate the proposed algorithm can improve the model learning accuracy compared with other benchmarks, and can perform better in energy shortage scenarios.…”
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Green Energy Strategic Management for Service of Quality Composition in the Internet of Things Environment by Jianhao Gao

“…The simulation results reveal that MFO has good optimization effect in the abovementioned models, and the optimization effect of MFO is improved by 8% and 6% compared with the genetic algorithm and particle swarm optimization, so as to realize the green energy strategic management of QoS composition in the environment of IoT.…”
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Radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism by Bei TANG, Qian WANG, Siguang CHEN

“…In order to fit the differentiated energy demands in vertical markets and ensure that internet of things (IoT) devices can hold an efficient and sustainable operation mode, a radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism was studied.Specifically, a system energy consumption minimization problem was formulated under the joint optimization consideration of computation offloading decision, uplink bandwidth resource allocation, downlink bandwidth resource allocation and base station power splitting.Meanwhile, by combining the concept of penalty function, a new evaluation index was introduced, and then an adaptive particle swarm optimization-based collaborative energy saving computation offloading (APSO-CESCO) algorithm was proposed to solve the problem.The proposed algorithm constructed dynamic inertia weight and linearly adjusted penalty factor, which could alternate the spatial distribution density of the particle community in real-time during the iterative search process, and the optimal computation offloading policy with tolerable punishment could be well-generated.Furthermore, to prevent particles from exceeding exploration range, the velocity boundary was introduced which could also reduce the generation probability of invalid solutions and improve the actual exploration effectiveness.Finally, the simulation results show that the proposed algorithm can achieve higher convergence efficiency and solution accuracy, and compared with other common benchmark schemes, the system energy consumption can be reduced by 34.09%, 14.72%, and 6.86%, respectively.…”
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Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China by Qunli Wu, Chenyang Peng

“…Results indicate that the proposed algorithm has higher precision and reliability than the coefficients optimized by other single-optimization methods, such as genetic algorithm, particle swarm optimization algorithm, or bat algorithm. …”
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考虑润滑状态的摆动活齿传动多目标优化设计 by 邓志平, 张均富

“…The global optimal solution is obtained based on chaotic particle swarm optimization algorithm. …”
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Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion by Ying Chen, Yuanning Liu, Xiaodong Zhu, Huiling Chen, Fei He, Yutong Pang

“…Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. …”
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高维多目标优化设计的灰色微粒群算法 by 车晓毅, 罗佑新, 汪超

“…A general finding solution method is introduced to high dimension multi-objective hybrid discrete variables and the multi-objective is transformed into single object with relative degree of grey incidences, and then the optimal solutions are found by improved particle swarm optimizer （ PSO）. …”
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Active micro-vibration isolation system for adaptive vibration suppression tests using piezoelectric stack actuator by Zhiyuan Gao, Lei Zhang, Tongxin Xu, Xiaojin Zhu

“…System identification is conducted using an improved particle swarm optimization method, specifically the Hybrid PSO-Jaya algorithm, which sequentially integrates the PSO algorithm with the Jaya algorithm. …”
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A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model by Wangping Li, Wanchang Zhang, Zhihong Li, Yu Wang, Hao Chen, Huiran Gao, Zhaoye Zhou, Junming Hao, Chuanhua Li, Xiaodong Wu

“…The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. …”
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基于正态反高斯模型的自适应小波消噪方法 by 吴国洋

“…A locally adaptive wavelet de-noising method based on normal inverse Gaussian modal is proposed.Firstly,the db5 wavelet is used to decompose the signal.For those wavelet coefficients which contain a lot of noise,the normal inverse Gaussian modal with good approximation property is constructed as the prior distribution model of those coefficients,on the basis of the model,Bayesian maximum a posteriori estimator is used to estimate the noisy wavelet coefficients and got the realistic wavelet coefficients.Then in the process of posteriori estimation,in order to get the best posteriori approximation model,the particle swarm optimization algorithm is used to select the key coefficient of the model.Finally,new wavelet coefficients are used for the reconstruction of the de-noised signal,and the de-noised signal is gotten.The algorithm is analyzed by simulation and bearing fault signal respectively.Analysis results show that this algorithm has good noise reduction effect,and can efficiently reduce the noise.…”
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Multiparameter Control Strategy and Method for Cutting Arm of Roadheader by Pengjiang Wang, Yang Shen, Xiaodong Ji, Kai Zong, Weixiong Zheng, Dongjie Wang, Miao Wu

“…The former part is designed using a backpropagation neural network that is optimized by an improved particle swarm optimization algorithm. …”
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Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID by Afef Badis, Mohamed Nejib Mansouri, Mohamed Habib Boujmil

“…The superiority of the proposed TLBO-based FOPID controller has been demonstrated by comparing the results with recently published optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). …”
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