An interval Type-2 fuzzy Fractional-Order PD-PI controller for frequency stabilization of islanded microgrids optimized with CO algorithm by Siavash Shirali, Saeed Zolfaghari Moghaddam, Mortaza Aliasghary

“…To address this challenge, this study proposes an interval type-2 fuzzy fractional-order PD-PI (IT2FFOPD-PI) controller with the capability to reduce frequency deviations. Additionally, optimizing the controller parameters in dynamic systems is another critical issue, where traditional algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and sine cosine algorithm (SCA) often struggle with premature convergence and suboptimal solutions. …”
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Optimized PID Controller for Load Frequency Control in Multi-Source and Dual-Area Power Systems Using PSO and GA Algorithms by Zhengwei Qu, Waqar Younis, Xianglin Liu, Abdul Khalique Junejo, Sulaiman Z. Almutairi, Peng Wang

“…A proportional integral derivative (PID) controller, tuned using particle swarm optimization (PSO) and genetic algorithm (GA) techniques, manages frequency in a thermal two-area tie-line IPS and a one-area multi-source power network. …”
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Optimal load frequency control system for two-area connected via AC/DC link using cuckoo search algorithm by Gaber EL-SAADY, Alexey MIKHAYLOV, Nora BARANYAI, Mahrous AHMED, Mahmoud HEMEIDA

“…Results: The CSA was compared with particle swarm optimization algorithm (PSO) under identical conditions. …”
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A Meta-Heuristic Algorithm-Based Feature Selection Approach to Improve Prediction Success for Salmonella Occurrence in Agricultural Waters by Murat Canayaz, Murat Demir, Zeynal Topalcengiz

“…Features were selected by using binary meta-heuristic optimization methods including differential evolution optimization (DEO), grey wolf optimization (GWO), Harris hawks optimization (HHO) and particle swarm optimization (PSO). …”
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Volt/VAr Regulation of the West Mediterranean Regional Electrical Grids Using SVC/STATCOM Devices With Neural Network Algorithms by H. Feza Carlak, Ergin Kayar

“…The modeled power system is optimized for the size and location of the FACTS devices by applying genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms to the selected busbars of the FACTS devices, a strategy designed to significantly reduce system losses. …”
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Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm by Babangida Modu, Md Pauzi Abdullah, Abdulrahman Alkassem, Mukhtar Fatihu Hamza

“…The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. …”
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Grey modeling method for approximate exponential sequence of optimizing initial condition by Yun YUE, Guangyue LU

“…Grey GM（1,1）prediction method is only suitable for the prediction model of the original sequence which satisfies the characteristic of the approximate exponential through the accumulated generating operation.In order to widen the application range of the traditional grey prediction model,a new method,dubbed DGM（1,1,c,β）model（direct grey model）,was proposed to improve the accuracy of grey GM（1,1）prediction by optimizing initial conditions.DGM（1,1,c,β）model was established for the original sequence conforming to the approximate exponential and the model parameters were obtained by the particle swarm optimization algorithm.Both the simulation and analysis of the example demonstrate that the proposed method is more effective and practical.…”
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Simulation-Based Two-Stage Scheduling Optimization Method for Carrier-Based Aircraft Launch and Departure Operations by Jue Liu, Nengjian Wang

“…To address the coupled challenges of path planning under spatial constraints and station matching/sequencing under operational constraints, we developed (1) a deep reinforcement learning (DRL)-based path planning algorithm (AAE-SAC), and (2) an enhanced particle swarm optimization (PSO) algorithm (LTA-HPSO). …”
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Optimizing Hyperspectral Desertification Monitoring Through Metaheuristic-Enhanced Wavelet Packet Noise Reduction and Feature Band Selection by Weichao Liu, Jiapeng Xiao, Rongyuan Liu, Yan Liu, Yunzhu Tao, Tian Zhang, Fuping Gan, Ping Zhou, Yuanbiao Dong, Qiang Zhou

“…Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. We applied Particle Swarm Optimization (PSO) to fine-tune the Wavelet Packet (WP) decomposition levels, thresholds, and wavelet basis function, ensuring optimal spectral decomposition and reconstruction. …”
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Multi-Parameter Optimization Using Metaheuristic Algorithms to Improve Uncrewed Aerial Vehicles’ Wireless Communications: A Performance Analysis by Lalan J. Mishra, Naima Kaabouch

“…In Approach 2, particle swarm optimization converged fastest, while gray wolf optimization was slowest. …”
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Evolutionary Cost Analysis and Computational Intelligence for Energy Efficiency in Internet of Things-Enabled Smart Cities: Multi-Sensor Data Fusion and Resilience to Link and Devi... by Khalid A. Darabkh, Muna Al-Akhras

“…When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. …”
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Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti

“…Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. …”
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Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses by da Pan

“…Experimental results demonstrate that SA achieves superior performance compared to genetic algorithms (GA), greedy approaches, ant colony optimization (ACO), and particle swarm optimization (PSO). …”
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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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Research on the connectivity reliability analysis and optimization of natural gas pipeline network based on topology by Xiuxuan Yang, Kun Chen, Minghui Liu

“…Case studies on a regional pipeline network (89 nodes, 98 segments) demonstrate that loop structures exhibit 25.7% higher average reliability ( $$\:{R}_{j}$$ = 0.87792) than branch nodes (v79: $$\:{R}_{j}$$ =0.60933). The AGA-driven optimization increases system-wide connectivity reliability ( $$\:{R}_{SU}$$ ) from 0.03 to 0.247 by strategically adding redundant pipelines (v71–v77), outperforming particle swarm optimization (PSO) by 65%. …”
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Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization by Feng Xiao, Biying Shi, Jie Gao, Huapeng Chen, Di Yang

“…Finally, based on the data from the pavement management system in Shanxi Province, it was verified that the CS-BNN model outperforms the genetic algorithm-BNN, particle swarm optimization-BNN, and BNN models in terms of the two metrics. …”
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KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration by Jing Liu, Zicheng Guo, Yalong Li

“…Moreover, the Kepler optimization (KO) algorithm is applied and benchmarked against flower pollination (FP) algorithm and particle swarm optimization (PSO) algorithm. …”
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Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application by Kuang Shi, Tingting Sun

“…The Relative Frequency Change Rate (RFCR) and Modal Assurance Criterion (MAC) were calculated as objective functions for PSA iteration; comparative studies were then conducted against Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of damage identification accuracy, computational efficiency, and noise robustness. …”
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Optimal Configuration of Multi-microgrid System with Multi-agent Joint Investment Based on Stackelberg Game by Ruiyuan PAN, Zhong TANG, Chenhao SHI, Minjie WEI, An LI, Weiyang DAI

“…Then, a Stackelberg game model is built to minimize the payoff function of the multi-microgrid system and maximize the revenue of distribution networks separately. In addition, an algorithm combining the adaptive genetic algorithm and particle swarm optimization is proposed to solve the optimal configuration of distributed power in the multi-microgrid system. …”
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Fusion of Visible and Infrared Images Using a Reinforcement Learning System Based on Fuzzy Logic and Convolution Optimized with Wild Horse Algorithm by Mahvash Zarimeidani, Amir Amirabadi, Nasrin Amiri, Iman Ahanian, Siavash Es’haghi

“…This hybrid reinforcement learning system was optimized using algorithms including wild horse optimization (WHO), genetic algorithm (GA), and particle swarm optimization (PSO) to improve specific fusion metrics such as image correlation, similarity coefficient, image entropy, and signal-to-noise ratio. …”
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