Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks by Umar Draz, Tariq Ali, Sana Yasin, Muhammad Hasanain Chaudary, Muhammad Ayaz, El-Hadi M. Aggoune, Isha Yasin

“…The hybridization techniques such as Elephant Herding Optimization (EHO) with Genetic Algorithm (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), and Binary Particle Swarm Optimization (BPSO) are used for optimization. …”
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Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm by Yanfang Zhang

“…Then, a hybrid fruit fly particle swarm algorithm based on a genetic optimization mechanism is proposed to achieve dynamic optimization seeking for parameters in local clustering to improve the clustering effect of local clustering. …”
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Application of Harris Hawks Optimization Algorithm in Optimization of Generalized Nonlinear Muskingum Parameters ——A Case Study of the Luohe River by CHEN Haitao, ZHAO Zhijie

“…The Muskingum model plays an important role in river flood simulation,and its simulation accuracy relies on the optimal selection of parameters.To address the current challenges in parameter calibration for the Muskingum model,such as complex solution processes and low accuracy,the use of the Harris Hawks optimization (HHO) algorithm was proposed to optimize its parameters.HHO algorithm has a wide range of global search capabilities,with fewer parameters to be adjusted.Taking Luohe River,a tributary of the Yellow River,as the research object,the generalized nonlinear Muskingum model was used to simulate the flood in the Yiyang-Baimasi section of the river.The parameters were optimized by employing the HHO algorithm,particle swarm optimization (PSO) algorithm,and ant colony optimization (ACO) algorithm,respectively.The results show that the generalized nonlinear Muskingum model based on the HHO algorithm achieved high simulation accuracy in the Yiyang-Baimasi section of the Luohe River,with a Min.SSD of 1 237 and the flood peak error (DPO) of only 5,outperforming those obtained through optimization using PSO algorithm and ACO algorithm.The results are suitable for application in flood forecasting in the Yiyang-Baimasi section of the Luohe River.…”
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Optimal Allocation of Hybrid Renewable Distributed Generation with Battery Energy Storage System Using MOEA/D-DRA Algorithm by P. Pon Ragothama Priya, S. Baskar, S. Tamil Selvi, C. K. Babulal

“…Simulation studies are conducted on IEEE 33-node and TNEB 84-node Radial Distribution Systems (RDSs), comparing results with the Rider Optimization Algorithm (ROA) and Hybrid Nelder Mead-Particle Swarm Optimization (HNMPSO) respectively. …”
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Ultrahigh-Dimensional Model and Optimization Algorithm for Resource Allocation in Large-Scale Intelligent D2D Communication System by Minxin Liang, Jiandong Liu, Jinrui Tang, Ruoli Tang

“…In addition, a novel evolutionary algorithm called the cooperatively coevolving particle swarm optimization with variable-grouping (VGCC-PSO) is developed, in which a novel mutation operation is introduced for ensuring fast satisfaction of constraints. …”
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Research on the Optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic Algorithm of Wolf Pack Predation by Qingdong Wu, Jijun Miao, Zhaohui Liu, Jiaxiu Chang

“…The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang’s calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. …”
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Behavior Analysis of the New PSO-CGSA Algorithm in Solving the Combined Economic Emission Dispatch Using Non-parametric Tests by Milena Gajić, Sanela Arsić, Jordan Radosavljević, Miroljub Jevtić, Bojan Perović, Dardan Klimenta, Miloš Milovanović

“…This paper proposes a new metahaeuristic algorithm named particle swarm optimization and chaotic gravitational search algorithm (PSO-CGSA) for solving the combined economic and emission dispatch (CEED) problem. …”
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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

“…The purpose of this study was to evaluate the performance of meta-heuristic optimization algorithms for feature selection to increase the Salmonella occurrence prediction success of commonly used algorithms in agricultural waters. …”
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An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment by G Logeswari, K Thangaramya, M Selvi, J. Deepika Roselind

“…A robust feature selection subsystem, employing Synergistic Dual-Layer Feature Selection (SDFC) algorithm, combines statistical methods, such as mutual information and variance thresholding, with advanced model-based techniques, including Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) are employed to identify the most relevant features. …”
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An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm by Wei Chen, Qike Cao, Bingyu Cao, Bo Jin

“…Comparative experiments with Improved Artificial Bee Colony Algorithm (IABC), Chaotic Adaptive Firefly Optimization Algorithm (CAFA), Adaptive Particle Swarm Optimization (APSO), and Lévy Flight Strategy Chaotic Snake Optimization Algorithm (LCSO) demonstrate that MSPOA improves network coverage by 5.85%, 11.33%, 21.05%, and 20.66%, respectively. …”
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A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks by Xiao-Xue Sun, Jeng-Shyang Pan, Shu-Chuan Chu, Pei Hu, Ai-Qing Tian

“…According to the experimental results, they demonstrate that it is more outstanding than the Pigeon-Inspired Optimization algorithm, the QUasi-Affine TRansformation Evolutionary algorithm, and particle swarm optimization algorithm. …”
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Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms by Olukunle Kolawole Soyinka, Monica Ngunan Ikpaya, Lumi Luka

“…This study solves this problem using Bio-inspired algorithms to tune the controller gain. To determine the required PID controller gain parameters this paper utilizes a Simulink model of a quadcopter combined with the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm (CSA) optimization respectively to minimize error in the attitude rate. …”
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Optimization of electric vehicle charging facility layout considering the enhancement of renewable energy consumption capacity and improvement of PSO algorithm by Di Zheng, Baobao Zheng

“…These three improvements include randomly updating inertia weights, introducing acceleration factors to replace learning factors, and introducing fast non-dominated sorting for better or worse selection, and improving the optimization ability of the algorithm by solving the crowding distance. The results showed that the maximum function values of the designed algorithm were 3.56 × 10–14, 5.32 × 100, and 1.08 × 101 for unimodal, multimodal, and composite functions, respectively, and the standard deviations of the algorithm were 2.01 × 10–14, 3.557 × 100, and 8.56 × 10–1, all of which were smaller than comparison algorithms. …”
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Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems by Thang Trung Nguyen, Thai Dinh Pham, Le Chi Kien, Le Van Dai

“…Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. …”
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Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm by Yang Yang, Huiwen Hou, Gang Yao, Bo Wu

“…To predict the VIV performance of a double-deck steel truss (DDST) girder with additional aerodynamic measures, the VIV response of a DDST bridge was investigated using wind tunnel tests and numerical simulation, a learning sample database was established with numerical simulation results, and a prediction model for the amplitude of the DDST girder and VIV parameters was established based on three machine learning algorithms. The optimization algorithm was selected using root mean square error (RMSE) and the coefficient of determination (R²) as evaluation indices and further improved with a genetic algorithm and particle swarm optimization. …”
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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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A Novel Optimization Algorithm Inspired by Egyptian Stray Dogs for Solving Multi-Objective Optimal Power Flow Problems by Mohamed H. ElMessmary, Hatem Y. Diab, Mahmoud Abdelsalam, Mona F. Moussa

“…The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. …”
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An Actor–Critic-Based Hyper-Heuristic Autonomous Task Planning Algorithm for Supporting Spacecraft Adaptive Space Scientific Exploration by Junwei Zhang, Liangqing Lyu

“…At the bottom level of the hyper-heuristic algorithm, this paper uses the particle swarm optimization algorithm, grey wolf optimization algorithm, differential evolution algorithm, and positive cosine optimization algorithm as the basic operators. …”
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Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO by Enlai ZHANG, Yi CHEN, Liang SU, Ruoyu ZHONGLIAN, Xianyi CHEN, Shangfeng JIANG

“…Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost, and PSO-XGBoost through data training. …”
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Artificial neural network algorithm for time dependent radiative Casson fluid flow with couple stresses through a microchannel by Pradeep Kumar, Felicita Almeida, Qasem Al-Mdallal

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