An improved SPWM control approach with aid of ant lion optimization for minimizing the THD in multilevel inverters by Alaa M. Abdel-hamed, Abdelrahman M. Nasser, Hamdy Shatla, Amr Refky

“…For verification, the performance and effectiveness of the ALO technique are assessed by comparing its results to those obtained using the simplified sinusoidal pulse width modulation (SSPWM) technique, genetic algorithm (GA), and particle swarm optimization (PSO) in existing literature. Simulation results verified the efficacy of ALO in finding the optimal parameters. …”
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Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization by Felipe A.C. Viana, Valder Steffen Jr., Marcelo A.X. Zanini, Sandro A. Magalhães, Luiz C.S. Góes

“…In the present formulation two nature-based methods, namely the Genetic Algorithms and the Particle Swarm Optimization were used. An optimization problem is formulated in which the objective function represents the difference between the measured characteristics of the system and its model counterpart. …”
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An Improved Topology of Isolated Bidirectional Resonant DC-DC Converter Based on Wide Bandgap Transistors for Electric Vehicle Onboard Chargers by Md. Tanvir Shahed, A. B. M. Harun-Ur Rashid

“…The PID controller parameters have been optimized using both the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm and a comparative analysis has been presented between the two algorithms. …”
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The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment by Liwen Zhang, Chao Zhang, Zhuo Sun, You Dong, Pu Wei

“…The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. …”
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Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. …”
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A study on the optimal allocation of photovoltaic storage capacity for rural new energy microgrids based on double-layer multi-objective collaborative decision-making by Huixuan Li, Peng Li, Xianyu Yue, Yongle Zheng, Wenjing Zu, Hongkai Zhang

“…The quantum-behaved particle swarm optimization algorithm is used to solve the optimal solution set of the objective function, and the interactive multi-criteria decision-making method is used to select the compromise solution to realize efficient optimal allocation of optical storage capacity. …”
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Ship Formation and Route Optimization Design Based on Improved PSO and D-P Algorithm by Peilong Xu, Dan Lan, Haolin Yang, Shengtian Zhang, Hyeonseok Kim, Incheol Shin

“…Based on this, a ship formation model combining improved particle swarm optimization algorithm and a generative route optimization method based on improved Douglas-Peucker algorithm are proposed. …”
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Optimizing planting management practices considering a suite of crop water footprint indicators —A case-study of the Fengjiashan Irrigation District by Yujie Yuan, Jichao Wang, Xuerui Gao, Kejing Huang, Xining Zhao

“…Subsequently, an optimization model of plant structure and management practices based on water footprint was developed, which was solved by multi-objective particle swarm optimization. Finally, the optimization scheme of crop planting management with low-water-consumption and low-pollution in FID was identified. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…The proposed methodology combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for hybrid feature selection, ensuring the selection of the most relevant features for detecting FDIAs. …”
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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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Integration of Multi-Source Landslide Disaster Data Based on Flink Framework and APSO Load Balancing Task Scheduling by Zongmin Wang, Huangtaojun Liang, Haibo Yang, Mengyu Li, Yingchun Cai

“…The present study proposes an innovative approach to integrate multi-source landslide disaster data by combining the Flink-oriented framework with load balancing task scheduling based on an improved particle swarm optimization (APSO) algorithm. It utilizes Flink’s streaming processing capabilities to efficiently process and store multi-source landslide data. …”
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Optimization of power system load forecasting and scheduling based on artificial neural networks by Jiangbo Jing, Hongyu Di, Ting Wang, Ning Jiang, Zhaoyang Xiang

“…Based on the load forecasting, a Particle Swarm Optimization (PSO) algorithm is employed to quickly determine the optimal scheduling strategy, ensuring the economic efficiency and safety of the power system. …”
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Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost by Dana A. Al-Qudah, Ala’ M. Al-Zoubi, Alexandra I. Cristea, Juan J. Merelo-Guervós, Pedro A. Castillo, Hossam Faris

“…Additionally, an automatic multi-language prediction approach for online restaurant reviews was proposed by combining the eXtreme gradient boosting (XGBoost) and particle swarm optimization (PSO) techniques for the ordinal regression of these reviews. …”
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Visible Light Broadband Achromatic Metalens Based on Variable Height Nanopillar Structures by Yongyang Li, Haiyang Huang, Cong Zhang, Xiangshuo Shang, Yang Liu, Junyan Hu, Dengyu Shan, Naiyun Tang, Wei Li

“…In this paper, a novel design method is used, which expands the parameter space by increasing the cross-sectional diversity of the metalens meta-atoms to provide the phase required for focusing different wavelengths, combined with particle swarm optimization for phase compensation. The multi-level metalens designed by this method achieves a constant and approximate focal length in the visible wavelength range of λ = 450–650 nm, with a polarization-independent absolute focusing efficiency of about 17%, and a numerical aperture (NA) of 0.31 for a lens diameter of 100 μm. …”
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Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope by Bozhou Li, Dajiang Ju, Xingwang Li, Yan Liu, Hongru He, Hao Wang

“…Then, based on the gated recurrent unit (GRU) model, the article applied the particle swarm optimization (PSO) algorithm to optimize the parameters of the GRU network and used the support vector machine (SVM) model to optimize the classification function of the network output. …”
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An optimized method for AUV trajectory model in benthonic hydrothermal area based on improved slime mold algorithm by Chunmeng Jiang, Yiming Tang, Jianguo Wang, Wenchao Zhang, Min Zhou, Jiaying Niu, Lei Wan, Guofang Chen, Gongxing Wu, Xide Cheng

“…The simulation experiments were conducted in comparison with the artificial fish swarm algorithm (AFSA), the particle swarm optimization (PSO), and the compact cuckoo search (CCS). …”
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Atomic Energy Optimization: A Novel Meta-Heuristic Inspired by Energy Dynamics and Dissipation by Mohammed Omari, Mohammed Kaddi, Khouloud Salameh, Ali Alnoman, Mohammed Benhadji

“…Notable applications of AEO to problems like the Rastrigin function and the Traveling Salesman Problem (TSP) showcase its effectiveness, with experimental results demonstrating superior convergence and robustness compared to traditional methods like Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Through extensive experimentation, AEO achieved up to a 20% faster convergence rate and a 15% improvement in solution quality over peer methods, demonstrating its superiority in solving benchmark optimization problems.…”
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Time Series Prediction of COD_Mn in Dianchi Lake Based on Data Decomposition and NARX Optimization by WANG Yongshun, CUI Dongwen

“…Comparative analyses are made with WPT-particle swarm optimization (PSO) - NARX, WPT-genetic algorithm (GA) - NARX, WPT-NARX, SHIO-NARX, WPT-SHIO extreme learning machine (ELM), and WPT-SHIO-BP neural network models. …”
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Co-Simulation Model for Determination of Optimal Active Power Filters Settings in Low-Voltage Network by Mario Primorac, Zvonimir Klaić, Heidi Adrić, Matej Žnidarec

“…The research in this paper is directed toward developing a co-simulation optimization model to determine optimal settings of the parallel APF due to harmonic reduction in a real low-voltage network using particle swarm optimization for 24 h intervals. The research in this paper was conducted on a real radial low-voltage feeder, where at each node, the variability of production and/or consumption is taken, which is obtained on the basis of real measurements and tests. …”
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Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm by Qi Guo, Rui Li, Changjiang Zheng, Gwanggil Jeon

“…Moreover, comparative analyses of various optimization methods on the custom-built dataset reveal that the ant colony optimization algorithm markedly outperforms the simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO). The method offers an innovative technical approach to vehicle path planning and is instrumental in advancing low-carbon and environmentally sustainable goals while enhancing transportation efficiency.…”
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