A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning by Muhammad Haris, Dost Muhammad Saqib Bhatti, Haewoon Nam

“…Particle Swarm Optimization (PSO) stands as a cornerstone among population-based swarm intelligence algorithms, serving as a versatile tool to tackle diverse scientific and engineering optimization challenges due to its straightforward implementation and promising optimization capabilities. …”
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Artificial Bee Colony Algorithm for Fresh Food Distribution without Quality Loss by Delivery Route Optimization by Sapna Katiyar, Rijwan Khan, Santosh Kumar

“…Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. …”
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An innovative complex-valued encoding black-winged kite algorithm for global optimization by Chengtao Du, Jinzhong Zhang, Jie Fang

“…Abstract The black-winged kite algorithm (BKA) constructed on the black-winged kites’ migratory and predatory instincts is a revolutionary swarm intelligence method that integrates the Leader tactic with the Cauchy variation procedure to retrieve the expansive appropriate convergence solution. …”
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Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump by Wenjie Wang, Majeed Koranteng Osman, Ji Pei, Shouqi Yuan, Jian Cao, Fareed Konadu Osman

“…A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. …”
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Design and Efficiency Optimization of Distributed Laser Wireless Power Transmission Systems Through Centralized Scheduling and Current Regulation by Liangrong Sun, Jinsong Kang, Yunfeng Bai, Pengjia Jin

“…To achieve optimal efficiency, a central scheduling controller is designed to regulate the current of LDs. A swarm intelligence-based optimization algorithm is used to determine the optimal operating current. …”
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Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model by Wenwen Cheng, Chen Yu, Xiaohui Liu

“…The Montreal Cognitive Assessment (MoCA) was used to test cognitive function. The swarm intelligence optimization algorithm (SIOA) was used to direct feature weighting and hyperparameter optimization, which were considered simultaneous activities. …”
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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

“…Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network (DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization (ARO) algorithm,eagle habitat optimization (EPO) algorithm,and DBN based on wavelet packet transform (WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO (particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.② The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.…”
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Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions by Shahad Alqefari, Mohamed El Bachir Menai

“…Intelligent optimization solutions, including evolutionary and swarm intelligence, provide high flexibility and performance in complex scenarios but require significant computational resources. …”
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ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection by Yu-Cai Wang, Jie-Sheng Wang, Min Zhang, Hao-Ming Song, Jia-Ning Hou, Yu-Liang Qi, Yu-Wei Song

“…In the second set of experiments, RReTSO CY is compared with other binary swarm intelligence optimization algorithms, with results indicating that the proposed method effectively reduces the feature subset size, improves classification accuracy and achieves the lowest average fitness value. …”
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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. …”
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EMNet: A Novel Few-Shot Image Classification Model with Enhanced Self-Correlation Attention and Multi-Branch Joint Module by Fufang Li, Weixiang Zhang, Yi Shang

“…In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. …”
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Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm by Lina Zhang, Ziyi Huang, Zhiyin Yang, Bo Yang, Shengpeng Yu, Shuai Zhao, Xingrui Zhang, Xinying Li, Han Yang, Yixing Lin, Helong Yu

“…The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. …”
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Risk prediction modeling for cardiorenal clinical outcomes in patients with non-diabetic CKD using US nationwide real-world data by Christoph Wanner, Johannes Schuchhardt, Chris Bauer, Meike Brinker, Frank Kleinjung, Tatsiana Vaitsiakhovich

“…Multivariable time-to-first-event risk prediction models were developed for each outcome using swarm intelligence methods. Model discrimination was demonstrated by stratifying cohorts into five risk groups and presenting the separation between Kaplan–Meier curves for these groups. …”
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RESEARCH ON SENSOR TEMPERATURE COMPENSATION SYSTEM BASED ON IMPROVED RBF NEURAL NETWORK by SONG RuiJuan

“…Considering the current temperature compensation method is used to establish the temperature compensation model using intelligent algorithm,and use swarm intelligent optimization algorithm to optimize and improve the compensation precision,has good compensation effect for nonlinear sensor temperature drift,but for the low efficiency of this method has good linearity,and the use of linear least squares fitting method the routine can get better compensation effect,so this will be the least squares fitting method and RBF neural network model integration,a model of temperature compensation of pressure sensor,using ant colony algorithm to optimize the conventional RBF neural network,improve the performance of compensation model. …”
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FAULT DIAGNOSIS OF THE PLANETARY GEARBOX BASED ON SFLA-BP MODEL AND KPCA FEATURE EXTRACTION by HE Yan, WANG ZongYan

“…The shuffled frog leaping algorithm（ SFLA） is a swarm intelligent optimization algorithm that combines competitive evolutionary strategy and limited random search. …”
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Reentry Trajectory Optimization for a Hypersonic Vehicle Based on an Improved Adaptive Fireworks Algorithm by Xing Wei, Lei Liu, Yongji Wang, Ye Yang

“…As a relatively new swarm intelligent algorithm, an adaptive fireworks algorithm (AFWA) has exhibited promising performance on some optimization problems. …”
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An improved particle swarm optimization for multilevel thresholding medical image segmentation. by Jiaqi Ma, Jianmin Hu

“…Replacing the original costly exhaustive search approach, swarm intelligent optimization algorithms are recently used to determine the optimal thresholds for medical image, and medical images tend to have higher bit depth. …”
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