Evaluation of an Information Flow Gain Algorithm for Microsensor Information Flow in Limber Motor Rehabilitation by Naiqiao Ning, Yong Tang

“…The support vector machine is used to classify the myoelectric signals and optimize the parameters in the support vector machine using the grid search method and particle swarm optimization algorithm and classify the test samples using the trained support vector machine. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). …”
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Design of circulating temperature control system for rubber and plastic industry based on mechatronics by Hexun Cao, Weiming Liu

“…Aiming at the problem of poor control effect of current temperature control system, a new circulating temperature control system for plastic industry was proposed in this study.MethodsFirstly, fuzzy neural network and improved particle swarm optimization algorithm were introduced in this study, and then the hybrid algorithm was combined with mechatronics technology to design and implement a set of rubber and plastic industry cycle temperature control system.ResultsAccording to the test data, the improved algorithm performed well in both scenarios. …”
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Secrecy-Constrained UAV-Mounted RIS-Assisted ISAC Networks: Position Optimization and Power Beamforming by Weichao Yang, Yajing Wang, Dawei Wang, Yixin He, Li Li

“…To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. …”
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SSA-ELM Hydrological Time Series Forecast Model Based on Wavelet Packet Decomposition and Phase Space Reconstruction by LI Lude, CUI Dongwen

“…Considering the nonlinear and multi-scale characteristics of hydrological time series,this paper proposes a squirrel search algorithm (SSA)-extreme learning machine (ELM) forecasting model based on wavelet packet decomposition (WPD) and phase space reconstruction.It is then applied to the Shangguo Hydrological Station in Yunnan Province for monthly runoff and precipitation forecasting.Specifically,WPD is performed to decompose the runoff and precipitation time series data,and the Cao method is applied to reconstruct the phase space of each subseries component.Then,the principle of SSA is outlined,and objective functions are constructed through the training samples of each component.The objective functions are optimized by SSA,and the results are compared with the optimization results of the whale optimization algorithm (WOA),the gray wolf optimization (GWO) algorithm,and the particle swarm optimization (PSO) algorithm.Finally,the weight of the ELM input layer and the hidden layer bias obtained by optimization based on SSA,WOA,GWO algorithm,and PSO algorithm,respectively,are utilized to build SSA-ELM,WOA-ELM,GWO-ELM,and PSO-ELM models,which,in addition to the unoptimized ELM models,are applied to forecast each subseries component,and the forecast results are summed and reconstructed to obtain the final forecasting results.The results show that SSA outperforms WOA,GWO algorithm,and PSO algorithm in optimizing the objective functions of each component and that it offers better optimization accuracy.The mean relative error,mean absolute error,mean square root error,and forecast pass rate of the proposed SSA-ELM model for monthly runoff and monthly precipitation forecast are 5.32% and 3.84%,0.078 m³/s and 0.169 mm,0.103 m³/s and 0.209 mm,97.5% and 95.8%,respectively,indicating that its forecasting accuracy is higher than that of other models such as the WOA-ELM model.…”
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Parameter Estimation for the One-Term (Multiterm) Fractional-Order SEIAR Models of Norovirus Outbreak by Tianzeng Li, Yu Wang, Weiqiu Pan

“…Then, we make use of the modified hybrid Nelder-Mead simplex search and particle swarm optimization (MH-NMSS-PSO) algorithm to obtain the fractional orders and parameters for these fractional-order SEIAR models of Norovirus outbreak. …”
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Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm by Xiaolei Zhang, Kaigong Zhao, Shang Gao, Changming Li

“…Additionally, a pre-allocated bee swarm algorithm is introduced to mitigate the issue of local incident points being unable to participate in rescue due to low weights, and a comparison of traditional genetic algorithms and particle swarm optimization algorithms is conducted. Experiments conducted in a virtual urban fire scenario validate the effectiveness of the proposed model. …”
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Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP by Xinming Zhang, Doudou Wang, Haiyan Chen, Wentao Mao, Shangwang Liu, Guoqi Liu, Zhi Dou

“…Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).…”
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Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach by Hassan S. Ahmed, Ahmed J. Abid, Adel A. Obed, Ameer L. Saleh, Reheel J. Hassoon

“…This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. …”
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A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet by Muhammad Waqas Ahmed, Moneerah Alotaibi, Sultan Refa Alotaibi, Dina Abdulaziz Alhammadi, Asaad Algarni, Ahmad Jalal, Jeongho Cho

“…These features are fused and refined using Particle Swarm Optimization (PSO) to create a robust and informative representation. …”
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Advanced control parameter optimization in DC motors and liquid level systems by Serdar Ekinci, Davut Izci, Mohammad H. Almomani, Kashif Saleem, Raed Abu Zitar, Aseel Smerat, Vaclav Snasel, Absalom E. Ezugwu, Laith Abualigah

“…Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO’s superior performance. …”
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Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties by Shuai Ma, Yafeng Wu, Zheng Hua, Linfeng Gou

“…The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression. …”
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Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms by M. Sundar Rajan

“…The centerpiece of this study is the introduction of a novel hybrid algorithm, HPSGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) techniques. …”
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An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy systems in distribution network by Fude Duan, Ali Basem, Sadek Habib Ali, Teeb Basim Abbas, Mahdiyeh Eslami, Mahdi Jafari Shahbazzadeh

“…Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. …”
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Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO by Umesh Kumar Lilhore, Surjeet Dalal, Magdalena Radulescu, Marinela Barbulescu

“…This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. …”
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Study on an Intelligent Prediction Method of Ticket Price in a Subway System with Public-Private Partnership by Shengmin Wang, Jun Fang, Lanjun Liu, Han Wu

“…Compared with other prediction methods (the price adjustment method based on PPP contract, the traditional BP algorithm, the BP neural network improved by the genetic algorithm, the BP algorithm improved by the particle swarm optimization, and the support vector machine), the model proposed in this paper showed better prediction accuracy and calculation stability.…”
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Low-threshold dual-polarization electro-optic nonlinear activation functions by Tao Jin, Jian Lin, Pengjun Wang, Qiang Fu, Yuhan Sun, Yi Zou, Shixun Dai, Weiwei Chen, Jun Li, Tingge Dai, Jianyi Yang

“…This scheme relies on a structure composed of an optical power splitter and a thermo-optic modulator based on Mach–Zehnder interferometer, which are optimized by using direct binary search and particle swarm optimization algorithms. When a fixed proportion of the input optical signal is transformed into the electrical signal and the remaining input optical signal is modulated by using the thermo-optic effect, the rectified linear unit activation function with a low-threshold power of 0.2 mW for transverse-electric polarization and the Gaussian activation function with a low-threshold power of 0.1 mW for transverse-magnetic polarization can be measured separately. …”
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An Intelligent Frequency Control Scheme for Inverting Station in High Voltage Direct Current Transmission System by Saleem, Muhammad Amir Raza, Syed Waqar Umer, Muhammad Faheem, Touqeer Ahmed Jumani, Muhammad Yameen

“…A broad simulation model of the HVDC transmission system is developed using MATLAB software to evaluate the effectiveness of the proposed controllers such as Adaptive Neuro‐Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and optimization of Proportional‐Integral‐Derivative (PID) controller using Particle Swarm Optimization (PSO) based control strategy for addressing the frequency instability problems. …”
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A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system by AL-Wesabi Ibrahim, Jiazhu Xu, Imad Aboudrar, Khaled Alwesabi, Li danhu, Hassan Z. Al Garni, Elhachemi Mammeri, Hossam Kotb, Mohit Bajaj, Shir Ahmad Dost Mohammadi

“…Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. …”
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Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization by Yong Wang, Yingying Yuan, Kevin Assogba, Ke Gong, Haizhong Wang, Maozeng Xu, Yinhai Wang

“…Thus, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are hybridized to propose GA-PSO heuristics. …”
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