Search Results - (( parameter optimization method algorithm ) OR ( variable optimization swarm algorithm ))
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1
Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization
Published 2014“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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2
Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
Published 2017“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm
Published 2023“…Ant colony optimization; Hydroelectric power; Hydroelectric power plants; Investments; Particle swarm optimization (PSO); Reservoirs (water); Stream flow; Water supply; Ant colony algorithms; Hydro-power generation; Hydropower reservoirs; Optimization algorithms; Particle swarm optimization algorithm; Reservoir performance; Streamflow generations; Uncertainty and variability; Genetic algorithms…”
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4
Metaheuristic Algorithm for Wellbore Trajectory Optimization
Published 2019“…This research will propose a new neighborhood function with Particle swarm optimization(PSO) algorithm for minimizing the true measured depth (TMD). …”
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5
Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
Published 2018“…To deal with these problems, this thesis introduces three approved intelligent controllers for hydropower generation. Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. …”
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A swarm intelligent approach for multi-objective optimization of compact heat exchangers
Published 2023Article -
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Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi
Published 2018“…Simulation results and their comparison with Particle Swarm Optimization based method show high performance and good ability of the proposed method in PMSM parameter estimation.…”
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8
On the optimal control of the steel annealing processes as a two-stage hybrid systems via PSO algorithms
Published 2009“…The heating and soaking furnaces of the steel annealing line form the two-stage hybrid systems. Three algorithms including particle swarm optimisation (PSO) with globally and locally tuned parameters (GLBest PSO), a parameter free PSO algorithm (pf-PSO) and a PSO-like algorithm via extrapolated PSO (ePSO) are considered to solve this optimal control problem for the two-stage steel annealing processes (SAP). …”
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9
Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization
Published 2022“…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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10
Single and Multiple variables control using Tree Physiology Optimization
Published 2017“…The proposed algorithm is also compared with deterministic gradient-free algorithm: Nelder-Mead simplex (NMS) and another metaheuristic algorithm: Particle Swarm Optimization (PSO). …”
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The use of heuristic ordering and particle swarm optimization for nurse scheduling problem
Published 2017“…A combination of heuristic ordering and particle swarm optimization (HOPSO) has been proposed to achieve the objectives. …”
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12
Solving the integrated inventory supply chain problems using meta-heuristic methods / Seyed Mohsen Mousavi
Published 2018“…A Modified Particle Swarm Optimization (MPSO) algorithm, a Genetic Algorithm (GA), a modified fruit fly optimization algorithm (MFOA) and a simulated annealing (SA) algorithm were used to find the optimal solution. …”
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13
A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…To summarise, metaheuristic algorithms can give a superior optimization approach than the traditional artificial neural network method, providing the computing time is within an acceptable range. …”
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14
Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”
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TUNING PROCESS OF SINGLE INPUT FUZZY LOGIC CONTROLLER BASED ON LINEAR CONTROL SURFACE APPROXIMATION METHOD FOR DEPTH CONTROL OF UNDERWATER REMOTELY OPERATED VEHICLE
Published 2013“…The variable parameter for signed distance method in SIFLC tuning by Particle Swarm Optimization (PSO) algorithm. …”
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Battery remaining useful life estimation based on particle swarm optimization-neural network
Published 2024“…Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSO NN) for estimating battery RUL. …”
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Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System
Published 2018“…The current study compared the performance of the SML model with popular evolutionary computing methods, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). …”
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18
Online PID controller tuning using tree physiology optimization
Published 2017“…A simulation of online PID-tuning is applied to demonstrate the effectiveness of proposed algorithm and compared with other optimization method which are Particle Swarm Optimization (PSO) and Ziegler-Nichols (Z-N) method. © 2016 IEEE.…”
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19
Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters
Published 2016“…Optimization of thin film coating parameters is important in identifying the required output.Two main issues of the process of physical vapor deposition (PVD) are manufacturing costs and customization of cutting tool properties.The aim of this study is to identify optimal PVD coating process parameters.Three process parameters were selected,namely nitrogen gas pressure (N2),argon gas pressure (Ar),and Turntable Speed (TT),while thin film grain size of titanium nitrite (TiN) was selected as an output response.Coating grain size was characterized using Atomic Force Microscopy (AFM) equipment.In this paper,to obtain a proper output result,an approach in modeling surface grain size of Titanium Nitrite (TiN)coating using Response Surface Method (RSM) has been implemented. …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed
Published 2012“…The application of FES optimized by GA on regionalization creates opportunities for further researches which utilizes different types of optimization like Ant Colony Optimization (ACO), ANN’s, Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA).…”
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