Denoising of electromagnetic data from different geological blocks using a hybrid PSO-GWO algorithm and CNN by Zhong-Yuan Liu, Zhong-Yuan Liu, Zhong-Yuan Liu, Di-Quan Li, Di-Quan Li, Di-Quan Li, Yecheng Liu, Yecheng Liu, Yecheng Liu, Xian Zhang, Xian Zhang, Xian Zhang

“…We propose a novel denoising approach that combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to optimize a Convolutional Neural Network (CNN). …”
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5G Network Deployment Planning Using Metaheuristic Approaches by Binod Sapkota, Rijan Ghimire, Paras Pujara, Shashank Ghimire, Ujjwal Shrestha, Roshani Ghimire, Babu R. Dawadi, Shashidhar R. Joshi

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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Integrated algorithm based on vectors in node localization for wireless sensor networks by WANG Yu-feng, WANG Yan

“…A location correction vector（LCV）was constructed by the differences between estimated distances and range measurements; an improved particle swarm optimization （PSO） was used to find correction steps of nodes; location correction equaled the value of LCV multiplying step. …”
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Photovoltaic energy harvesting booster under partially shaded conditions using MPPT based sand cat swarm optimizer by Moch Rafi Damas Abdilla, Novie Ayub Windarko, Bambang Sumantri

“…The suggested SCSO performance is evaluated under a variety of weather situations, including both instances of partially shaded and uniform irradiance. The SCSO results are juxtaposed with other existing bio-inspired algorithms, such as grey wolf optimization (GWO), particle swarm optimization (PSO), and tunicate swarm algorithm (TSA). …”
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Research on Quality Prediction for Thermal Printing Using a Particle Swarm Optimization with Back Propagation (PSO-BP) Neural Network by Chun-Ling Ho, Zhiyun Wu, Tung-Chiung Chang, Shenjun Qi

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A novel hybrid algorithm based on combined BICA-BPSO for solving the optimal electricity procurement problem for large consumers by Mehdi Mehdinejad, Sayyad Nojavan, Kazem Zare, Behnam Mohammadi-ivatloo

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Optimization method of building energy efficiency design based on decomposition multi objective and agent assisted model by Bai Chaoqin, Yang Zhuoyue

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Optimal Power Flow Based on a Metaheuristics Optimization Approach for the Iraqi Super High Voltages Network by Ali Almosawi, Mesut Cevik, Aysel Ersoy

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Modeling of Global and Individual Kinetic Parameters in Wheat Straw Torrefaction: Particle Swarm Optimization and Its Impact on Elemental Composition Prediction by Ismael Urbina-Salas, David Granados-Lieberman, Martín Valtierra-Rodríguez, Claudia Adriana Ramírez-Valdespino, David Aarón Rodríguez-Alejandro

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Research on Grid-Connected Speed Control of Hydraulic Wind Turbine Based on Enhanced Chaotic Particle Swarm Optimization Fuzzy PID by Yujie Wang, Yang Cao, Zhong Qian, Jianping Xia, Xuhong Kang, Yixian Zhu, Yanan Yang, Wendong Zhang, Shaohua Chen, Guoqing Wu

“…In the enhanced algorithm, a Circle chaotic mapping is combined with particle swarm optimization (PSO) to prevent PSO from becoming trapped in local optima. …”
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Optimizing capacitor bank placement in distribution networks using a multi-objective particle swarm optimization approach for energy efficiency and cost reduction by Mehrdad Ahmadi Kamarposhti, Raymond Ghandour, Mahmoud Abdel-Aty, Mohamed Hafez, Mohanad Alfiras, Shawkat Alkhazaleh, Ilhami Colak, Ahmed Solyman

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Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm by Hui Zhuo, Tongren Li, Wei Lu, Qingsong Zhang, Lingyun Ji, Jinliang Li

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Multi-Timescale Nested Hydropower Station Optimization Scheduling Based on the Migrating Particle Whale Optimization Algorithm by Mi Zhang, Guosheng Zhou, Bei Liu, Dajun Huang, Hao Yu, Li Mo

“…To address these limitations, this paper proposes the Migrating Particle Whale Optimization Algorithm (MPWOA), which initializes the population using chaotic mapping, incorporates a particle swarm mechanism to enhance exploitation during the spiral predation phase, and integrates the black-winged kite migration mechanism to improve stochastic search performance. …”
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A comparative approach of single-objective optimization algorithms for energy control of flexible manufacturing system by Marius-Adrian PĂUN, Eugenia MINCĂ, Sergiu Stelian ILIESCU, Nicoleta ARGHIRA, Florin DRAGOMIR

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Optimized solar PV integration for voltage enhancement and loss reduction in the Kombolcha distribution system using hybrid grey wolf-particle swarm optimization by Awot Getachew Abera, Tefera Terefe Yetayew, Assen Beshr Alyu

“…A hybrid optimization approach combining Particle Swarm Optimization and Grey Wolf Optimization algorithms is proposed for determining optimal sizing and placement of PV-based DGs. …”
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Cable Force Optimization of Circular Ring Pylon Cable-Stayed Bridges Based on Response Surface Methodology and Multi-Objective Particle Swarm Optimization by Shengdong Liu, Fei Chen, Qingfu Li, Xiyu Ma

“…This study proposes a hybrid approach combining Response Surface Methodology (RSM) and Multi-Objective Particle Swarm Optimization (MOPSO) to overcome these challenges. …”
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Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO by Bo Qiu, Jian Zhang, Yun Yang, Guangyuan Qin, Zhongyi Zhou, Cunrui Ying

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Application research on classification and integration model of innovation and entrepreneurship education resources based on GNN-PSO algorithm by Yongjian Dong

“…Therefore, this study proposes a classification and integration model of innovation and entrepreneurship education resources based on GNN-PSO (graph neural networks and particle swarm optimization). The model uses the powerful feature extraction ability of GNN to dig deep into the internal relationship between educational resources. …”
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