Search Results - (( parameter optimization method algorithm ) OR ( based simulation case algorithm ))
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1
Genetic algorithm based method for optimal location placement of flexible ac transmission system devices for voltage profile improvement
Published 2011“…This thesis present a genetic algorithm based method for placement of FACTS devices for voltage profile improvement. …”
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2
Optimal power flow using the Jaya algorithm
Published 2016“…Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. …”
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3
Simulated real-time controller for tuning algorithm using modified hill climbing approach
Published 2014“…Often, it is necessary to calibrate a certain parameters of a control system due to plant parameters fluctuation over time.In this research, an intelligent algorithmic tuning technique suitable for realtime system tuning based on hill climbing optimization algorithm and model reference adaptive control system (MRAC) technique is proposed. …”
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4
Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab
Published 2025“…The research improved the predictive models by integrating them with the Genetic Algorithm (GA). The optimizer algorithm (i.e., GA) determines the optimal input variables and internal parameters in the prediction models. …”
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5
Computational dynamic support model for social support assignments around stressed individuals among graduate students
Published 2020“…The algorithm complexity analysis was used to measure the execution time in the worst case. …”
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6
SINR improvement using Firefly Algorithm (FA) for Linear Constrained Minimum Variance (LCMV) beamforming technique
Published 2023Conference Paper -
7
Vehicle pick-up and drop-off schedule optimization in a university setting
Published 2024“…Lateness will be removed using a lateness waiting time rollback mechanism. A simulated annealing-based multi-directional iterative local search algorithm is employed for solution optimization. …”
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8
Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
Published 2015“…However, in some cases, as the iteration number increases the result of PSO–KS method is comparable with ABC–KS method.…”
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9
A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
Published 2025“…A smart memory-based strategy is incorporated into the algorithm to enhance solution optimality, convergence properties, and exploitation capabilities. …”
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10
Neural Network Model and Finite Element Simulation of Spring back in Plane-Strain Metallic Beam Bending
Published 2006“…To validate the finite element model physical experiments were conducted. A neural network algorithm based on the backpropagation algorithm has been developed. …”
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11
Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter
Published 2015“…However, in some cases, as the iteration number increases the result of PSO-KS method is comparable with ABC-KS method. …”
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12
Numerical simulation and experimental verification on distortions induced by wire-arc additive manufacturing components and costing analysis / Keval Priapratama Prajadhiana
Published 2024“…The result with optimized parameters and boundary conditions were transferred towards second case study which is a Pipe Flange WAAM component based on ASME B16.5 standard where the development of numerical simulation and fabrication of WAAM parts followed similar procedures with the first case study. …”
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13
Fuzzy modelling and control for a nonlinear reboiler system of a distillation column
Published 2006“…Model parameters are tuned using Genetic algorithm (GA) and Recursive least square (RLS). …”
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14
Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method
Published 2016“…Since the proposed models (like similar models in the literature) are likely to fall into local optimum points, a Branch and Bound based heuristic, a hybrid Simulated Annealing and Genetic algorithm, a hybrid Tabu search and Simulated Annealing, a hybrid Genetic algorithm and Simulated Annealing, a hybrid Ant Colony Optimization and Simulated Annealing and a hybrid Multi-layer Perceptron and Simulated Annealing algorithms are developed. …”
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15
Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…Performance indicators included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Parameter Magnitude-based Information Criterion 2 (PMIC2). …”
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16
Improved genetic algorithm for direct current motor high speed controller implemented on field programmable gate array
Published 2019“…There are many researches have been done to optimize PI controller based evolutionary algorithm, such as Genetic Algorithm (GA). …”
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17
Development of a multi criteria decision support system using convolutional neural network and jaya algorithm for water resources management / Chong Kai Lun
Published 2021“…Apart from that, having low values in four of the performance criteria: RMSE, MAE, NSE, and RSR, have further strengthened the credibility of the results. As for the optimization process, the reservoir operation rule was derived using a meta-heuristic algorithm at the monthly interval. …”
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18
Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
Published 2010“…It is observed that the APFLC showed convincing performance over the entire simulation of the Pico-satellite. Genetic Algorithm (GA) is a computational model inspired by evaluation. …”
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19
Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks
Published 2025thesis::doctoral thesis -
20
Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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