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1
Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm
Published 2022“…Therefore, this study proposes a modified smoothed function algorithm (MSFA) based method to tune the FOPID controller of AVR system since it requires fewer number of function evaluation per iteration. …”
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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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Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm
Published 2020“…The ASED-based method provided better tracking performance than the SED method by obtaining the objective function’s lower values. …”
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4
PID Tuning Method Using Chaotic Safe Experimentation Dynamics Algorithm for Elastic Joint Manipulator
Published 2021“…CSEDA was a modified version of the safe experimentation dynamics algorithm (SEDA) that used a chaos function in the updated equation. …”
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Improved smoothed functional algorithmsoptimized pid controller for efficient speed regulation of wind turbines
Published 2025“…This study introduces a novel approach for PID controller tuning in wind turbine systems using single-agent optimization methods, specifically the memory smoothed functional algorithm (MSFA) and norm-limited smoothed functional algorithm (NL-SFA). …”
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An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system
Published 2023“…Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods.…”
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Optimized PID controller of DC-DC buck converter based on archimedes optimization algorithm
Published 2023“…The converter's core function is to regulate output voltage, ensuring stability despite load fluctuations and input voltage changes. …”
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Design and Implementation of PID Controller for the Cooling Tower?s pH Regulation Based on Particle Swarm Optimization PSO Algorithm
Published 2025“…The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning. ? …”
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Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network
Published 2022“…Finally, an AHP was integrated with FA to form Firefly Analytical Hierarchy Algorithm (FAHA) to automatically calculate the weight of each objective function based on the load flow outputs followed by the optimisation process. …”
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10
Optimization of a robust sigmoid PID controller for automatic voltage regulation using the nonlinear sine-cosine algorithm with amplifier feedback dynamic weighted (AFDW) system
Published 2025“…The given paper presents a robust Sigmoid-based Proportional-Integral-Derivative (SPID) controller for Automatic Voltage Regulator (AVR) systems, optimized using the Nonlinear Sine Cosine Algorithm (NSCA) enhanced with the Amplifier Feedback Dynamic Weighted (AFDW) system. …”
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Data-driven PID controller of wind turbine systems using safe experimentation dynamics algorithm
Published 2024“…SEDA, a single-agent based optimization technique, requires only one function evaluation per iteration, alleviating computational burdens. …”
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12
In silico prediction and validation of Gfap as miR-3099 target in the mouse brain
Published 2017“…Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. …”
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Monotone Fuzzy Rule Interpolation for Practical Modeling of the Zero-Order TSK Fuzzy Inference System
Published 2022“…We apply the MFRI algorithm to a failure mode and effect analysis case study and a tanker ship heading regulation problem. …”
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Enhancing obfuscation technique for protecting source code against software reverse engineering
Published 2019“…The proposed technique can be enhanced in the future to protect games applications and mobile applications that are developed by java; it can improve the software development industry. …”
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15
Nonlinear dynamic system identification and control via self-regulating modular neural network
Published 2003“…An endeavor is made in this paper to describe a self-regulating constructive multi-model neural network called Self-regulating Growing Multi-Experts Network (SGMN) that can approximate an unknown nonlinear function from observed input-output training data. …”
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Genetic algorithm for control and optimisation of exothermic batch process
Published 2013“…As a result, improved multivariable genetic algorithm (IMGA) with adaptable fitness function ability is introduced in this work. …”
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An improved electromagnetism-like mechanism algorithm for the optimization of maximum power point tracking / Tan Jian Ding
Published 2017“…The search scheme began with relatively bigger steps. The algorithm then systematically tuned the step sizes based on a specially designed nonlinear equation. …”
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Investigation of firefly algorithm and chaos firefly algorithm for load prequency control / Zaid Najid
Published 2015“…This project analyzes the performance of the algorithms based LFC in three power system area. The primary objectives of LFC are to maintain frequency and minimize power interchanges with neighboring control areas. …”
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20
Empowering Energy-Sustainable IoT Devices With Harvest Energy-Optimized Deep Neural Networks
Published 2024“…A novel training was proposed for Deep Neural Network (DNN) algorithms chain rules to minimize the loss function based on updating the parameters of the weights hidden layer and convergence training to achieve near-optimal performance and minimize unneeded label data. …”
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