Intelligent Control of Power System Stabilizer Based on Archimedes Optimization Algorithm – Feed Forward Neural Network

Aribowo, Widi; Muslim, Supari; Suprianto, Bambang; Haryudo, Subuh Isnur; Hermawan, Aditya Chandra

doi:10.22266/ijies2021.0630.05

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…The goal of the research is to come up with a hybrid AOA-NN method to make power system stabilizers work better. Experimental results outperformed FFNN, CFNN, DTDNN, and STSA-NN method Aribowo et al [ 31 ] Measurement (Elsevier) Adaptive variational mode decomposition based on Archimedes optimization algorithm and its application to bearing fault diagnosis Rotating machinery fault diagnosis Variational mode decomposition (VMD) is frequently employed in rotating equipment fault diagnostics. It is a widely used method all over the world.…”

Section: Application Of Aoamentioning

confidence: 99%

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Dhal

Ray

Rai

et al. 2023

Arch Computat Methods Eng

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The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes Optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes Optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired Optimization Algorithms (PIOA), the Archimedes Optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

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Section: Application Of Aoamentioning

confidence: 99%

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Dhal

Ray

Rai

et al. 2023

Arch Computat Methods Eng

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“…Classical swarm intelligence algorithms include particle swarm optimization (PSO) 13 , artificial bee colony (ABC) 14 , grey wolf optimization (GWO) 15 , harris hawk algorithm (HHO) 16 , and whale optimization algorithm (WOA) 17 . With the continuous development and update of technology, swarm intelligence algorithms have excelled in problems such as positioning computation 18 , path planning for travelers 19 , support vector machine optimization 20 , robotic route finding 21 , power system control 22 , and optimization of routing protocols for the Internet of Things (IoT) 23 .…”

Section: Introductionmentioning

confidence: 99%

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Wang,

Cao,

Chen

et al. 2024

Sci Rep

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To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

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“…English network teaching methodologies focus on various computational and statistical techniques such as neural networks, Bayesian networks, and machine learning algorithms, there is a lack of explicit discussion on methodologies for teaching English language skills through network-based approaches [1]. English network teaching involves utilizing network structures to facilitate language learning, including vocabulary acquisition, grammar comprehension, and language proficiency development [2]. Incorporating network-based teaching methodologies could offer innovative and interactive ways to engage learners, personalize learning experiences, and track progress over time [3].…”

Section: Introductionmentioning

confidence: 99%

Application of Frbf Network Autonomous Learning Algorithm Based on Intrinsic Motivation in English Network Teaching

Chen

2024

jes

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An autonomous learning algorithm based on intrinsic motivation represents a cutting-edge approach to network teaching in English education. By harnessing intrinsic motivation, this algorithm empowers learners to autonomously engage with English language materials and activities, driven by their innate curiosity and desire for mastery. Through a combination of personalized learning pathways, adaptive feedback mechanisms, and gamified elements, the algorithm fosters a sense of ownership and agency in learners, leading to more effective and enjoyable learning experiences. Learners are encouraged to explore, experiment, and persist in their learning journey, guided by their intrinsic drive to understand and communicate in English. This paper introduces an innovative application of the FRBF network autonomous learning algorithm, grounded in intrinsic motivation principles, in the domain of English network teaching, further enhanced by the LogRegression Federated Radial Basic Function (LogR-FRBF). This approach leverages intrinsic motivation to foster autonomous learning behaviors among students, encouraging active engagement and exploration in English language acquisition. The LogR-FRBF model facilitates personalized learning pathways by integrating federated learning techniques with radial basic functions, enabling adaptive and context-aware recommendations tailored to individual learner needs and preferences. Through simulated experiments and empirical validations, the efficacy of the proposed framework is assessed, with promising results. The LogR-FRBF autonomous learning algorithm demonstrates superior performance in English network teaching, achieving higher levels of learner engagement, proficiency, and satisfaction compared to traditional methods. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience.

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Intelligent Control of Power System Stabilizer Based on Archimedes Optimization Algorithm – Feed Forward Neural Network

Cited by 11 publications

References 11 publications

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Application of Frbf Network Autonomous Learning Algorithm Based on Intrinsic Motivation in English Network Teaching

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