Modified aquila optimizer for forecasting oil production

Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Fan, Hong; AlRassas, Ayman Mutahar; Elaziz, Mohamed Abd

doi:10.1080/10095020.2022.2068385

Cited by 56 publications

(19 citation statements)

References 58 publications

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“…single-tuned, second-order, third-order and C-type damped filters). In addition, for solving PPF design problem, many constraints and objective functions are proposed [ 189 ]. Furthermore, a new technique based on the Pareto front and BA was developed to solve PPF problem.…”

Section: Applications Of Bat Algorithm By Area Of Disciplinementioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

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Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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Section: Applications Of Bat Algorithm By Area Of Disciplinementioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

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“… Al-Qaness et al (2022a) developed a marine predator algorithm with additional mutation operators (named MPAmu) to improve ANFIS in estimating wind power using time-series datasets from wind turbines located in France. Moreover, a combined metaheuristic-machine learning method based on aquila optimizer opposition-based learning and ANFIS, named AOOBL-ANFIS, has been used for the oil production forecasting ( Al-Qaness et al, 2022b ).…”

Section: Potential Challenges During Covid-19 Pandemicmentioning

confidence: 99%

Multivariate time-series blood donation/demand forecasting for resilient supply chain management during COVID-19 pandemic

Shokouhifar

Ranjbarimesan

2022

Cleaner Logistics and Supply Chain

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“…Recently, optimization techniques such as Particle Swarm Optimization (PSO) [27] have been used to solve selected problems across several domains [28]. These techniques are known to emulate natural evolution in their operations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

et al. 2022

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We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods.

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Modified aquila optimizer for forecasting oil production

Cited by 56 publications

References 58 publications

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Multivariate time-series blood donation/demand forecasting for resilient supply chain management during COVID-19 pandemic

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

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