Articles

Permanent URI for this collection

https://dspaceusad7.4science.cloud/handle/20.500.12458/2

Browse

Browsing Articles by Author "Abd Elaziz, Mohamed"

Now showing 1 - 6 of 6
  • Publication
    A hybrid Harris Hawks optimizer for economic load dispatch problems
    (2023)
    Al-Betar, Mohammed Azmi
    ;
    Awadallah, Mohammed A.
    ;
    Makhadmeh, Sharif Naser
    ;
    Abu Doush, Iyad
    ;
    Abu Zitar, Raed 
    ;
    Alshathri, Samah
    ;
    Abd Elaziz, Mohamed
    This paper proposes a hybridized version of the Harris Hawks Optimizer (HHO) with adaptive-hill-climbing optimizer to tackle economic load dispatch (ELD) problems. ELD is an important problem in power systems that is tackled by finding the optimal schedule of the generation units that minimize fuel conceptions under a set of constraints. Due to the complexity of ELD search space, as it is rigid and deep, the exploitation of HHO is improved by hybridizing it with a recent local search method called adaptive-hill climbing. The HHO can navigate several potential search space regions, while adaptive-hill climbing is used to deeply search for the local optimal solution in each potential region. To evaluate the proposed approach, six versions of ELD cases with various complexities and constraints have been used which are the 6 generation units with 1263 MW of load demand, 13 generation units with 1800 MW of load demand, 13 generation units with 2520 MW of load demand, 15 generation units with 2630 MW of load demand, 40 generation units with 10500 MW of load demand, and 140 generation units with 49342 MW of load demand. Furthermore, the proposed algorithm is evaluated on two ELD real-world cases which are 6 units-1263 MW and 15units-2630 MW. The results show that the proposed algorithm can achieve a significant performance for the majority of the experimented cases. It can achieve the best-reported solution for the ELD case with 15 generation units when compared to 15 well-established methods. Additionally, it obtains the second-best for the ELD case with 140 generation units when compared to 10 well-established methods. In conclusion, the proposed method can be an alternative to solve ELD problems which is efficient.
    Scopus© Citations 33  43  10
  • Publication
    Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
    (2022)
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    ;
    Abd Elaziz, Mohamed
    ;
    Yousri, Dalia
    ;
    Al-qaness, Mohammed A. A.
    ;
    Ewees, Ahmed A.
    This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA.
    Scopus© Citations 9  34  1
  • Publication
    Erratum to: Recent Advances of Chimp Optimization Algorithm: Variants and Applications
    (2023)
    Daoud, Mohammad Sh.
    ;
    Shehab, Mohammad
    ;
    Abualigah, Laith
    ;
    Alshinwan, Mohammad
    ;
    Abd Elaziz, Mohamed
    ;
    Shambour, Mohd Khaled Yousef
    ;
    Oliva, Diego
    ;
    Alia, Mohammad A.
    ;
    Abu Zitar, Raed 
      20
  • Publication
    Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System
    (2024)
    Abualigah, Laith
    ;
    Ahmed, Saba Hussein
    ;
    Almomani, Mohammad H.
    ;
    Abu Zitar, Raed 
    ;
    Alsoud, Anas Ratib
    ;
    Abuhaija, Belal
    ;
    Hanandeh, Essam Said
    ;
    Jia, Heming
    ;
    Abd Elminaam, Diaa Salama
    ;
    Abd Elaziz, Mohamed
    With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively.
    Scopus© Citations 8  16
  • Publication
    Recent Advances of Chimp Optimization Algorithm: Variants and Applications
    (2023)
    Daoud, Mohammad Sh.
    ;
    Shehab, Mohammad
    ;
    Abualigah, Laith
    ;
    Alshinwan, Mohammad
    ;
    Abd Elaziz, Mohamed
    ;
    Shambour, Mohd Khaled Yousef
    ;
    Oliva, Diego
    ;
    Alia, Mohammad A.
    ;
    Abu Zitar, Raed 
    Chimp Optimization Algorithm (ChOA) is one of the recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also, it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence. Therefore, the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using ChOA have been overviewed and summarized. Initially, introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of ChOA are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of ChOA are discussed in detail which are categorized into modified, hybridized, and paralleled versions. The main applications of ChOA are also thoroughly described. The applications belong to the domains of economics, image processing, engineering, neural network, power and energy, networks, etc. Evaluation of ChOA is also provided. The review paper will be helpful for the researchers and practitioners of ChOA 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.
      25Scopus© Citations 6
  • Publication
    Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques
    (2022)
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    ;
    Almotairi, Khaled H.
    ;
    Hussein, Ahmad MohdAziz
    ;
    Abd Elaziz, Mohamed
    ;
    Nikoo, Mohammad Reza
    ;
    Gandomi, Amir H.
    Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.
    Scopus© Citations 88  100  41