Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease by Tiantian Bai, Mengru Xu, Taotao Zhang, Xianjie Jia, Fuzhi Wang, Xiuling Jiang, Xing Wei

“…This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search algorithm, flower pollination algorithm, Harris hawk optimization algorithm, particle swarm optimization algorithm, and genetic algorithm) with machine learning technology to improve the early diagnosis of cardiovascular disease. …”
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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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Spatial Position Optimization of Components in the Labeling Robot System for Bundled Bars by Zhang Fuxiang, Zheng Yu, Huang Yongjian, Wang Chunmei, Huang Fengshan

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Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm by Rui Liang, Dingzhao Li, Haixin Sun, Liangpo Hong

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A multi-swarm greedy selection enhanced fruit fly optimization algorithm for global optimization in oil and gas production by Yang Gao, Liang Cheng

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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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Optimization of machining process route for internal joint parts using artificial fish swarm algorithm by Jun HAN, Junwei ZHU, Yang ZHANG, Zhenyao ZHAO, Zexi AN

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Tabu Genetic Cat Swarm Algorithm Analysis of Optimization Arrangement on Mistuned Blades Based on CUDA by Yi Cai, Junjie Gu, Honggang Pan, Hongyuan Zhang, Tianyu Zhao

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Application of sparrow search swarm intelligence optimization algorithm in identifying the critical surface in slope-stability by Abhijit Saha

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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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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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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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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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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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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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