An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures by Bin Li, Xuewen Rong, Yibin Li

“…For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. …”
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A study on monthly sales forecasting of new energy vehicles in urban areas using the WOA-BiGRU model. by Xiangtu Li

“…Its prediction results are compared with those of the particle swarm optimization (PSO) algorithm. The research findings are as follows: The growth of NEV sales has reversed the declining trend of overall automobile sales in China; Cities with higher NEV sales are predominantly concentrated in four major economic hubs--the Pearl River Delta, Yangtze River Delta, Beijing-Tianjin-Hebei region, and Chengdu-Chongqing. …”
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Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies by Behrouz Alefy, Vahid Babazadeh

“…The study applies state-of-the-art machine learning techniques to establish dominant patterns and offer forecasts using a wide range of student records. Weevil Damage Optimization Algorithm, Black Widow Optimization Algorithm, and Phasor Particle Swarm Optimization form the core of 3 optimization approaches. …”
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Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration by XIE Mande, HUANG Zhufang, SUN Hao

“…Then, a multi-user subtask scheduling scheme was proposed and an improved simulated annealing particle swarm algorithm was designed to minimize the total system cost to achieve the optimal offloading decision. …”
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Scalable and energy-efficient task allocation in industry 4.0: Leveraging distributed auction and IBPSO. by Qingwen Li, Tang Wai Fan, Lam Sui Kei, Zhaobin Li

“…It also introduces an improved variant of the improved Binary Particle Swarm Optimization (IBPSO) algorithm to manage complicated tasks that require multi-robot collaboration. …”
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Global maximum power point tracking method for photovoltaic systems using Takagi-Sugeno fuzzy models and ANFIS approach by N. Hadjidj, M. Benbrahim, D. Ounnas, L. H. Mouss

“…In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point. …”
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Reflection Reduction on DDR3 High-Speed Bus by Improved PSO by Huiyong Li, Hongxu Jiang, Bo Li, Miyi Duan

“…Additionally, an improved particle swarm optimization algorithm with adaptive perturbation is presented to solve the impedance mismatch problem (IPSO-IMp) based on the above model. …”
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Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination by Ying Liu, Lihua Huang

“…The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. …”
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Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism by Ramsha Rizwan, Farrukh Aslam Khan, Haider Abbas, Sajjad Hussain Chauhdary

“…Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. …”
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A SIR Model with Incomplete Data for the Analysis of Influenza A Spread in Ningbo by Bingqing Wang, Yanyao Hu, Zhongdi Cen, Jian Huang, Tianfeng He, Aimin Xu

“…Based on the incompleteness of the data, the parameter estimation problem of the SIR model is transformed into an optimization problem. The Particle Swarm Optimization algorithm is used to solve the optimization problem. …”
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A PSO Driven Intelligent Model Updating and Parameter Identification Scheme for Cable-Damper System by Danhui Dan, Yanyang Chen, Bin Xu

“…This paper suggests a cable model updating calculation scheme driven by the particle swarm optimization (PSO) algorithm. By establishing a finite element model considering the static geometric nonlinearity and stress-stiffening effect firstly, an automatically finite element method model updating powered by PSO algorithm is proposed, with the aims to identify the cable force and relevant parameters of cable-damper system precisely. …”
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Data-Driven Superheating Control of Organic Rankine Cycle Processes by Jianhua Zhang, Xiao Tian, Zhengmao Zhu, Mifeng Ren

“…Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy (QMEE) is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization (PSO) algorithm is applied to obtain optimal control law by minimizing the performance index. …”
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Network slicing deployment method based on isolation level by Qirun PAN, Kaizhi HUANG, Wei YOU

“…In order to balance the performance isolation requirements and security isolation requirements of network slicing,a network slice deployment method based on isolation level was proposed.The method first determined the isolation level of the network slice instance from the aspects of performance isolation and security isolation.When deploying the virtual nodes in the appropriate location,not only ensured that all network slice instances could reach their respective performance levels and security levels,but also isolated them.The gradation difference started to limit the coexistence condition of the virtual node.Then the integer linear programming method was used to model the problem,and the deployment cost was minimized as the objective function.Finally,the particle swarm optimization algorithm based on genetic algorithm was used to find the final deployment result.The simulation results show that the method has lower deployment cost and higher benefit-to-cost ratio,and can guarantee both performance and security.…”
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Evaluation of Teaching Quality of English Courses in Comprehensive Universities under Multiple Indicators by Haiying Song

“…In this paper, based on the analysis of the evaluation subjects of university English teaching quality, 16 evaluation indexes are constructed from five aspects: teaching content, teaching method, teaching process, teaching literacy, and teaching effect, and the Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM) algorithm is used to comprehensively evaluate the teaching quality, and finally English teaching in universities is selected as the object of empirical research. …”
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Joint Radio Resource Allocation and Base Station Location Selection in OFDMA Based Private Wireless Access Networks for Smart Grid by Peng Du, Yuan Zhang

“…Then, we propose scheduling and power control based algorithms for the radio resource allocation subproblem and K-means, simulated annealing (SA), and particle swarm optimization (PSO) based algorithms for the base station (BS) location selection subproblem and iterate over these two sets of algorithms to solve the target problem. …”
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Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach by Xu Bao, Yanqiu Li, Jianmin Li, Rui Shi, Xin Ding

“…In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. …”
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Hardware-in-loop implementation of an adaptive MPPT controlled PV-assisted EV charging system with vehicle-to-grid integration by Surabhi Singh, Hari Om Bansal

“…This paper developed and compared perturb and observe (P&O), Particle swarm optimization (PSO), and hybrid PSO + Adaptive neuro-fuzzy inference system (ANFIS) based algorithm for MPPT. …”
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Performance of Fitness Functions Based on Natural Frequencies in Defect Detection Using the Standard PSO-FEM Approach by Xiao-Lin Li, Roger Serra, Julien Olivier

“…A popular approach is to adopt the particle swarm optimization (PSO) algorithm. In this process, the fitness function is a critical factor in the success of the PSO-FEM approach. …”
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A Single-Way Ranging Localization of AUVs Based on PSO of Outliers Elimination by Xinnan Fan, Zhongjian Wu, Jianjun Ni, Chengming Luo

“…The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. …”
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An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems by Ebru Efeoğlu

“…Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. …”
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