A modified genetic algorithm with a new crossover mating scheme

Delima, Allemar Jhone P.; Sison, Ariel M.; Medina, Ruji P.

doi:10.52549/ijeei.v7i2.1047

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…In order to find an ideal solution to a problem in a complex space, it is necessary to find a compromise between two goals: to explore better solutions and to aggressively exploit the search space. Analytical studies have shown that GAs manage this trade-off optimally [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Comparison of Fuzzy-PI and Genetic Control Algorithms for Active and Reactive Power Flow between the Stator (DFIG) and the Grid

Guediri¹,

Touil²

2022

Eng. Technol. Appl. Sci. Res.

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This paper performs a comparison between Fuzzy-PI regulators and Genetic Algorithm (GA) for controlling an active and reactive Doubly-Fed Induction Generator (DFIG) for providing power to the electrical grid. Theoretical analysis, modeling, and simulation studies are provided. Control strategies were developed for both active and reactive forces in order to optimize energy production. The performance of the two control strategies was examined and compared using benchmarks for durability and reference traceability. This paper studied a system consisting of a wind turbine operating at variable wind speed and a two-feed asynchronous machine (DFIG) connected to the grid by the stator and fed by a transducer at the side of the rotor. The conductors were separately controlled for active and reactive power flow between the stator (DFIG) and the grid, which was achieved in this article using conventional PI and fuzzy logic controllers. The considered controllers generated reference voltages for the rotor to ensure that the active and reactive power reached the required reference values. This was done in order to ensure effective tracking of the optimum operating point and the maximum output of electrical power. System modeling and simulation were examined in Matlab/Simulink. Dynamic analysis of the system was performed under variable wind speed.

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Section: Introductionmentioning

confidence: 99%

Modeling and Comparison of Fuzzy-PI and Genetic Control Algorithms for Active and Reactive Power Flow between the Stator (DFIG) and the Grid

Guediri¹,

Touil²

2022

Eng. Technol. Appl. Sci. Res.

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An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

Delima¹

2020

IJACSA

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The K-Nearest Neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employed variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The Genetic Algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem, which is its mating scheme bounded on its crossover operator. Thus, the use of the novel Inversed Bi-segmented Average Crossover (IBAX) was observed. In the present work, the crossover improved genetic algorithm (CIGAL) was instrumental in the enhancement of KNN's prediction accuracy. The use of the unmodified genetic algorithm had removed 13 variables; while the CIGAL then further removed 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improved the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN); the use of the lone KNN algorithm, the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique revealed a 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. The above results show that the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.

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A modified genetic algorithm with a new crossover mating scheme

Cited by 2 publications

References 21 publications

Modeling and Comparison of Fuzzy-PI and Genetic Control Algorithms for Active and Reactive Power Flow between the Stator (DFIG) and the Grid

Modeling and Comparison of Fuzzy-PI and Genetic Control Algorithms for Active and Reactive Power Flow between the Stator (DFIG) and the Grid

An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

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