Improved Northern Goshawk Optimization Algorithm for Global Optimization

Sadeeq, Haval Tariq; Abdulazeez, Adnan Mohsin

doi:10.1109/icoase56293.2022.10075576

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…Two scenarios are simulated. In Scenario, ONR problem is solved using basic NGO [24] and stochastic fractal search (SFS) [28], harmony search algorithm (HAS) [29], and artificial rabbits algorithm (ARO) [30] and compared with INGO [25]. In Scenario-2, three case studies are compared considering: (a) only RESs penetration, (b) only EVs penetration and (c) both RESs and EVs penetration.…”

Section: Resultsmentioning

confidence: 99%

“…By having these modifications, the performance of basic NGO is improved significantly in comparison with various other meta-heuristics as proved in [25].…”

Section: Improved Northern Goshawk Optimizationmentioning

confidence: 97%

“…Levy flight (LF) is added to the NGO algorithm to boost the search capabilities for difficult real-world issues. LF has improved resource search efficiency hence they have been included in the INGO [25]. The second portion of Eqs.…”

Section: Improved Northern Goshawk Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

2024

IJIES

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In today's electrical distribution systems (EDS), the rise of renewable energy sources (RESs) and electric vehicles (EVs) holds immense significance. Despite their positive environmental impact, these technologies pose challenges due to their unpredictable nature. Efficiency and reliability are top priorities for EDSs, which can be achieved through optimal network reconfiguration (ONR). This approach addresses integrating varying RESs and EVs levels by considering distribution losses and the line loadability index (LLI). To enhance network performance by identifying optimal branches and tie-lines for switching, a novel meta-heuristic algorithm, the northern goshawk optimization algorithm (NGO), is proposed. To boost its search capabilities, an improved version, called improved NGO (INGO), introduces a levy flight distribution and an adaptive parameter. Simulations on unified Egyptian network (UEN) system across diverse scenarios demonstrate INGO's computational efficiency surpassing basic NGO and other algorithms like stochastic fractal search (SFS), harmony search algorithm (HAS), and artificial rabbits algorithm (ARO). INGO outperforms in target function optimization and computation time, showcasing its adaptability for real-time applications through reduced losses and enhanced loadability. In the initial network configuration, real power losses were measured at 805.73 kW and reactive power losses at 361.18 kVAr. Notably, the lowest voltage magnitude was recorded at bus-30, reaching 0.9463 p.u. The reliability index SAIDI was calculated as 3.148, corresponding to a maximum loadability of 1.73101 p.u. Following optimal reconfiguration with INGO, significant enhancements were observed. Losses reduced notably to 768.37 kW for real power and 360.86 kVAr for reactive power. The lowest voltage at bus-25 increased to 0.9511 p.u. Furthermore, SAIDI improved to 3.001, while loadability increased to 1.73903 p.u. These improvements were consistent even with varying PV and EV load penetrations.

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Section: Resultsmentioning

confidence: 99%

“…By having these modifications, the performance of basic NGO is improved significantly in comparison with various other meta-heuristics as proved in [25].…”

Section: Improved Northern Goshawk Optimizationmentioning

confidence: 97%

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Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

2024

IJIES

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“…Normalization thus ensures that each feature contributes approximately proportionately to the final prediction. In the case of the mentioned dataset, which likely contains a variety of features, normalization would ensure that no single feature dominates the model due to its scale, leading to more accurate and stable model performance [43].…”

Section: Preprocessing: Normalizationmentioning

confidence: 99%

Comprehensive Classification of Iris Flower Species: A Machine Learning Approach

Renas Rajab Asaad,

M. Abdulazeez

2024

ijcs

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This study employs robust machine learning techniques to comprehensively assess the classification of Iris flower species. This study investigates the effectiveness of several machine learning algorithms in reliably classifying Iris flower species by utilizing a dataset that includes crucial morphological attributes such as sepal length, sepal width, petal length, and petal width. The algorithms under consideration are Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Every algorithm has its own distinct methodology for classification, where Decision Trees offer clear interpretability and Random Forest and XGBoost offer strong and complex ensemble techniques. The primary aim of this study is to assess and contrast different algorithms, considering not only their classification accuracy but also significant performance metrics including precision, recall, F1-score, ROC AUC, and specificity. This research provides valuable insights into the capabilities and constraints of each methodology when implemented on a meticulously organized and defined botanical dataset. It is expected that the results of this study will contribute significantly to the fields of artificial intelligence and botanical taxonomy, highlighting the capacity of these methods to accurately identify and categorize plant species.

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“…The data classification or identification process involves the utilization of a support-vector engine to assign labels to instances, drawing from a variety of factors. In agricultural contexts, Multi-SVM has been employed to classify various crop diseases and flower varieties, hence showcasing its versatility across a range of domains [42], [43]. The utilization of Multi-SVM in the classification of fetal health based on cardiotocogram data entails the categorization of different fetal health states.…”

Section: Multisvmmentioning

confidence: 99%

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

Alkurdi,

Abdulazeez

2024

ijcs

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In the realm of obstetrics, the evaluation of fetal health remains a paramount yet challenging endeavor. Traditional approaches, such as electronic fetal monitoring (EFM), despite their widespread adoption, continue to grapple with uncertainties regarding their impact on neonatal outcomes and the reduction of emergency cesarean deliveries. This ambiguity is compounded by a prevailing confusion within the obstetric community about interpreting fetal heart rate patterns, often leading to inconsistent and subjective assessments. Addressing these complexities, our study presents an innovative machine learning-based techniques for the comprehensive classification of fetal health using cardiotocogram (CTG) data, offering a more objective and nuanced alternative to conventional methods. The core of our proposed solution is a novel model employing a sophisticated ensemble of machine learning classifiers, including Multi-Support Vector Machine (Multi-SVM), Decision Tree, Random Forest with Hyperparameter Tuning, XGBoost, and Neural Networks. This model is unique in its application, processing datasets in four different forms: raw datasets, datasets processed with MinMaxScaler, datasets subjected to feature selection using SelectKBest, and a combination of MinMaxScaler processing and SelectKBest feature selection. Such meticulous preprocessing, encompassing normalization and feature selection, is pivotal in ensuring equitable contribution from each feature, thereby optimizing the model's learning process and predictive accuracy. The effectiveness of our model is rigorously evaluated using a dataset comprising 2126 individual records from CTG exams, classified by specialist obstetricians into three types: Normal, Suspect, and Pathological. These records are exhaustively analyzed using various metrics, including Accuracy, Precision, Recall, F1-Score, ROC AUC, and Confusion Matrix. Among the classifiers, XGBoost emerged as the most proficient, consistently outperforming others across multiple metrics. This indicates its superior ability to accurately identify and categorize the different states of fetal health. Our findings thus underscore the significant promise of machine learning in revolutionizing fetal health monitoring, offering a more reliable, objective, and comprehensive method for assessing fetal well-being, with profound implications for prenatal care and clinical decision-making.

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Improved Northern Goshawk Optimization Algorithm for Global Optimization

Cited by 10 publications

References 23 publications

Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

Hybridization of Improved Northern Goshawk Optimization and Line Loadability Index for Reconfiguration Considering Solar and Electric Vehicles

Comprehensive Classification of Iris Flower Species: A Machine Learning Approach

Comprehensive Classification of Fetal Health Using Cardiotocogram Data Based on Machine Learning

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