Search Results - (( simulation optimization using algorithm ) OR ( using simulation model algorithm ))
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1
A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…Abstract This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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4
Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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Thesis -
5
Ant colony optimization for rule induction with simulated annealing for terms selection
Published 2012“…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
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Conference or Workshop Item -
6
Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)
Published 2008“…Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multi-objective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. …”
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Proceeding Paper -
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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE
Published 2020“…This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. …”
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Final Year Project -
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Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq
Published 2016“…The first algorithm was based on the traditional simulation of reservoir operation, whilst the second algorithm (Salg) enhanced the GAOM by changing the population values of GA through a new simulation process of reservoir operation. …”
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A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
Published 2013“…In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. …”
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Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems
Published 2016“…Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). …”
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DESIGN OPTIMIZATION OF A BLDC MOTOR BY GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2007“…Single and multi-objective functions of the motor are derived based on the steady state mathematical model. A constrained optimization on the objective function is performed using Genetic Algorithm (GA) and Simulated Annealing (SA), and optimal parameters are obtained. …”
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Conference or Workshop Item -
15
Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm
Published 2013“…Experimental data of Taguchi orthogonal array L9 was used to train the ANN model. The simulation results were evaluated and verified with the experiment. …”
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Teletraffic performance of mobile cellular channel assignment using genetic algorithm
Published 2023Conference paper -
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Simulation and Visualization of TSP Using Ant Colony Optimization
Published 2023“…The study concludes that optimization can be applied using programming language to provide users with comfortable and intelligible simulations and visualizations.…”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
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Design optimization of a bldc motor by genetic algorithm and simulated annealing
Published 2007“…Single and multi-objective functions of the motor are derived based on the steady state mathematical model. A constrained optimization on the objective function is performed using Genetic Algorithm (GA) and Simulated Annealing (SA), and optimal parameters are obtained. …”
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Conference or Workshop Item -
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BSKF: Binary Simulated Kalman Filter
Published 2015“…A set of traveling salesman problems are used to evaluate the performance of the proposed BSKF against Binary Gravitational Search Algorithm (BGSA) and Binary Particle Swarm Optimization (BPSO).…”
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