295 research outputs found
Correction to: Fractional-order chaotic oscillator-based Aquila optimization algorithm for maximization of the chaotic with Lorentz oscillator (Neural Computing and Applications, (2023), 35, 29, (21645-21662), 10.1007/s00521-023-08945-8)
No full textIn this article, the affiliation 11 for the Author Laith Abualigah was missing. The original article has been updated accordingly. © 2023, Springer-Verlag London Ltd., part of Springer Nature
Multi-objective Stochastic Paint Optimizer (MOSPO)
No full textThe single-objective version of stochastic paint optimizer (SPO) is appropriately changed to solve multi-objective optimization problems described as MOSPO. Color theory, the color wheel, and color combination methods are the main concepts of SPO. The SPO will be able to do excellent exploration and exploitation thanks to four simple color combination rules that do not have any internal parameters. Principles like using of fixed-sized external archive make the recommended technique various from the initial single-objective SPO. In addition, to perform multi-objective optimization, the leader selection feature has been added to SPO. The efficiency of recommended multi-objective stochastic paint optimizer (MOSPO) is tested on ten mathematical (CEC-09) and eight multi-objective engineering design problems concerning remarkable precision and uniformity compared to multi-objective particle swarm optimization (MOPSO), multi-objective slap swarm algorithm (MSSA), and multi-objective ant lion optimizer. According to the results of different performance metrics, such as generational distance (GD), inverted generational distance (IGD), maximum spread, and spacing, the proposed algorithm can provide quality Pareto fronts with very competitive results with high convergence.No Full Tex
An intelligent tuning scheme with a master/slave approach for efficient control of the automatic voltage regulator
No full textA new master/slave model driven, and an optimization algorithm-based proportional–integral–derivative (PID) plus second-order derivative (PIDD2) controller is proposed in this work for a stable and efficient operation of an automatic voltage regulator (AVR) system. In this context, an ideal reference model of Bode is used as a master model. The new improved optimization algorithm is constructed via integrating the Lévy flight mechanism into Runge–Kutta optimizing algorithm. This algorithm optimally tunes the PIDD2 controller with the aid of a cost function known as integral of squared error. The latter control mechanism forms the slave model. As the PIDD2 controller and the intelligent tuning algorithm attempt to follow the response dictated by the ideal reference model of the master model, a significant improvement is achieved for the efficiency and the stability of the AVR system. The proposed master/slave driven, and intelligent optimization algorithm-based PIDD2 control approach presents more excellent transient response (steady state error, rise time, settling time, peak time, percent overshoot), frequency response (gain margin, phase margin and bandwidth), robustness and stability. Nonideal conditions such as measurement noise and the saturation at the input of the generator in the AVR are also considered to demonstrate the efficacy of the proposed method. Furthermore, the existing fifty-eight techniques in the literature are also used for performance comparison in order to present the more excellent efficiency of the proposed method from a wider perspective.No Full Tex
A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language
No full textWe 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
Multi-objective chaos game optimization
No full textThe Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite its advantages, this method can only tackle problems formulated with one objective. The multi-objective CGO proposed in this study is utilized to handle the problems with several objectives (MOCGO). In MOCGO, Pareto-optimal solutions are stored in a fixed-sized external archive. In addition, the leader selection functionality needed to carry out multi-objective optimization has been included in CGO. The technique is also applied to eight real-world engineering design challenges with multiple objectives. The MOCGO algorithm uses several mathematical models in chaos theory and fractals inherited from CGO. This algorithm's performance is evaluated using seventeen case studies, such as CEC-09, ZDT, and DTLZ. Six well-known multi-objective algorithms are compared with MOCGO using four different performance metrics. The results demonstrate that the suggested method is better than existing ones. These Pareto-optimal solutions show excellent convergence and coverage.Full Tex
Enhancing Real-Time Data Analysis through Advanced Machine Learning and Data Analytics Algorithms
No full textThis paper investigates the amalgamation of sophisticated machine learning and data analytics algorithms to enhance real-time data analysis across diverse domains. Specifically, it concentrates on the utilization of machine learning methods for real-time data analysis, encompassing supervised, unsupervised, and reinforcement learning algorithms. The research underscores the significance of instantaneous processing, analysis, and decision-making in contemporary data-centric environments spanning industries like defense, exploration, public policy, and mathematical science. The paper explores data analytics strategies for real-time data analysis, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics techniques are explored for summarizing and visualizing extensive sensor data, while diagnostic analytics methodologies focus on detecting anomalies and irregular patterns in real-time data streams. Predictive analytics endeavors to predict forthcoming events based on historical data trends, thereby enabling proactive decision-making. Lastly, prescriptive analytics provides decision recommendations and optimization tactics grounded in predictive models and constraint logic. By offering a comprehensive examination of machine learning techniques and data analytics methodologies, the paper furnishes insights into augmenting real-time data analysis capabilities across various sectors. Additionally, it presents a case study on processing real-time data from an environmental monitoring system, illustrating the practical application of advanced machine learning and data analytics algorithms for proactive decision-making and environmental management
APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL
No full textGenetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms
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