Search Results - (( using simulation tool algorithm ) OR ( parameter optimization based algorithm ))
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1
A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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2
Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems
Published 2022“…Simulation experiments were run using different parameters to analyze the performance of the proposed algorithm with the system. …”
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Thesis -
3
OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…A constrained optimization on the objective function is performed using GA and SA, and optimal parameters are derived. …”
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Citation Index Journal -
4
OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING
Published 2009“…A constrained optimization on the objective function is performed using GA and SA, and optimal parameters are derived. …”
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5
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 -
6
Impact of evolutionary algorithm on optimization of nonconventional machining process parameters
Published 2025“…This paper presents the optimization of laser beam machining in additive manufacturing of polymer-based material parameters, specifically focusing on cutting speed, gas pressure of nitrogen, and focal point locations, to achieve optimal mean surface roughness. …”
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7
Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. …”
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8
Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm
Published 2022“…Nevertheless, many existing optimization tools for tuning the FOPID controller, which are based on multi-agent based optimization, require large number of function evaluation in their algorithm that could lead to high computational burden. …”
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9
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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10
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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11
Data-driven PID controller of wind turbine systems using safe experimentation dynamics algorithm
Published 2024“…However, existing optimization tools, especially those using multi-agent optimization, often entail a high computational burden due to a large number of function evaluations (NFE). …”
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Conference or Workshop Item -
12
Enhancing quality of service in Multi-Radio Multi-Channel Wireless Mesh Networks
Published 2023“…This research utilizes the OmNET++ 4.6 simulator, an open-source tool in C++, to evaluate proposed algorithms effectively. …”
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13
Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm
Published 2020“…Data-driven tools are an optimization method to find the optimal controller parameters using the system’s input and output data. …”
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14
Fast PID tuning of AVR system using memory-based smoothed functional algorithm
Published 2024“…This study introduces a novel approach using a memory-based smoothed functional algorithm (MSFA) to tune the PID controller in AVR systems. …”
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15
Optimization of operational policies for the Minab Reservoir, Southern Iran
Published 2012“…These parameters were optimized to reduce the water requirement based on the cost and benefit by using the Lingo model. …”
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16
Efficient vehicle routing optimization for autistic users / Abdulrahman Hasan Mohammed Shabalah
Published 2020“…The design and analysis of the comparison have been done using MATLAB tool. The experimental results showed that the performance of the proposed AFAC algorithm outperformed the classical Ant Colony and the recent MPAC algorithms. …”
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17
Bio-lubricant production based on optimum epoxidized oleic acid using in situ peracid mechanism / Tunku Arif Zafri Tunku Ozir
Published 2022“…Lastly, the kinetic reaction rates were determined using genetic algorithm MATLAB optimization tool and the experimental data was compared together with the simulation data.…”
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Student Project -
18
A comparative study for parameter selection in online auctions
Published 2009“…Hence, this work attempts to improve an existing bidding strategy by taking into accounts the evolution of various model of generic algorithm in optimizing the parameter of the bidding strategies. …”
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19
A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
Published 2025“…To ensure fair comparisons, parameter configurations for all algorithms are automated using the parameter tuning tool iterated racing (irace). …”
Review -
20
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…This article proposes an effective machine learning (ML) approach to achieve the optimal design of the charging track, considering the cross-coupling effect. The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”
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