Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model by Kerang Cao, Chang Liu, Siqi Yang, Yuxin Zhang, Lili Li, Hoekyung Jung, Shuo Zhang

“…In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. …”
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Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning by Elmoundher Hadjaidji, Mohamed Cherif Amara Korba, Khaled Khelil

“…This study aims to refine PD detection systems by introducing customized weighting to acoustic features, adjusting their significance based on their correlation with the disease and the classification algorithm utilized. The particle swarm optimization algorithm is employed for this purpose, with the Oxford PD dataset serving as the source data for training and validation. …”
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Improving the Performance of Steering by Wire Using a Model Predictive Controller Enhanced with Particle Swarm Optimisation by Taher Alhassany, Ali Hussien Mary, Furat Ibrahim Hussein, Mohammed Ghufran Khidhir Abboosh, Muhammad Umar Fareed

“…The current study introduces an innovation of a model that integrates model predictive control (MPC) with particle swarm optimisation (PSO) to improve the performance of SBW systems. …”
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Improved empirical wavelet transform combined with particle swarm optimization-support vector machine for EEG-based depression recognition by Yongxin Wang, Longqi Xu, Hongxu Qian, Haijun Lin, Xuhui Zhang

“…Therefore, there is a pressing need to develop techniques for detecting early signs of depression to enable timely intervention and potentially improve recovery rates. In this paper, we propose an improved method for the early objective diagnosis of depression utilizing an empirical wavelet transform (EWT) technique enhanced by a particle swarm optimization-support vector machine (PSO-SVM) algorithm. …”
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Adaptive fractional-order Darwinian particle swarm optimization algorithm by Tong GUO, Ju-long LAN, Yu-feng LI, Shi-wen CHEN

“…To overcome such shortcom-ing, an adaptive fractional-order Darwinian particle swarm optimization (AFO-DPSO) algorithm was proposed. …”
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Adaptive fractional-order Darwinian particle swarm optimization algorithm by Tong GUO, Ju-long LAN, Yu-feng LI, Shi-wen CHEN

“…To overcome such shortcom-ing, an adaptive fractional-order Darwinian particle swarm optimization (AFO-DPSO) algorithm was proposed. …”
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Simple gravitational particle swarm algorithm for multimodal optimization problems. by Yoshikazu Yamanaka, Katsutoshi Yoshida

“…Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). …”
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Building Construction Design Based on Particle Swarm Optimization Algorithm by Wenxue Song

“…In order to take a scientific risk control strategy to reduce the safety risk of construction projects, a construction safety risk decision-making method based on particle swarm optimization algorithm was proposed. …”
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Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization by Jomana Yousef Khaseeb, Arabi Keshk, Anas Youssef

“…Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. …”
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Risk Assessment of Supplier R&D Investment Based on Improved BP Neural Network by Yinghua Song, Xiaoyan Sang, Zhe Wang, Hongqian Xu

“…By leveraging the ability of particle swarm optimization (PSO), whale optimization algorithm (WOA), and genetic algorithm (GA) to search for global optimal solutions, the BP neural network is improved to avoid becoming trapped in local optimal solutions and enhance the model’s generalization ability. …”
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Improving Frequency Control of Multi-Area Interconnected Hydro-Thermal Power System Using PSO Algorithm by Dao Huy Tuan, Van Nguyen Ngoc Thanh, Dat Nguyen Chi, Van Huy Pham

“…This paper explores a method that combines traditional PID control with the Particle Swarm Optimization (PSO) algorithm to improve frequency regulation in interconnected hydropower systems. …”
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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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Determining Appropriate Strategy for Managing the Energy Production System in the Presence of Renewable Energy Sources and Improved Intelligence Method by Amir Sohel, Md Faisal Bin Ashab

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Research on Optimal Configuration of Offshore Wind Power Energy Storage Based on Improved Scene Clustering Algorithm by Jingui YI, Ziwei ZHU, Qing XIE

“…Furthermore, to minimize the annual comprehensive cost of the power storage, the paper constructs an optimal configuration model for offshore wind power storage and uses the particle swarm optimization algorithm to solve the optimal configuration model. …”
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Error Analysis and Compensation of 3‒PTT Parallel Robot by CHEN Mingfang, LIANG Hongjian, WEI Songpo, HE Chaoyin

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Improved Tuna Swarm Optimization (ITSO) Algorithm based on Adaptive Fitness-Weight for Global Optimization by A. F. Seini Yussif, T. Seini, C. Adu

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Improved artificial fish-swarm algorithm based on adaptive vision for solving the shortest path problem by Xian-min MA, IUN L

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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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Electromyography-based gesture recognition for quadriplegic users using hidden Markov model with improved particle swarm optimization by Xanno K Sigalingging, Alrezza Pradanta Bagus Budiarsa, Jenq-Shiou Leu, Jun-ichi Takada

“…The signals are then processed using a novel algorithm that employs hidden Markov model and improved particle swarm optimization to classify the gesture. …”
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