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Dr. Laith Abualigah
Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan

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0 Artificial Intelligence
0 Big Data Analytics
0 Information Retrieval
0 Machine Learning
0 Optimization

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Journal article
Published: 30 August 2021 in Processes
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Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO is pretty good, while the exploration capability is far from satisfactory. Considering the characteristics of these two algorithms, an improved hybrid AO and HHO combined with a nonlinear escaping energy parameter and random opposition-based learning strategy is proposed, namely IHAOHHO, to improve the searching performance in this paper. Firstly, combining the salient features of AO and HHO retains valuable exploration and exploitation capabilities. In the second place, random opposition-based learning (ROBL) is added in the exploitation phase to improve local optima avoidance. Finally, the nonlinear escaping energy parameter is utilized better to balance the exploration and exploitation phases of IHAOHHO. These two strategies effectively enhance the exploration and exploitation of the proposed algorithm. To verify the optimization performance, IHAOHHO is comprehensively analyzed on 23 standard benchmark functions. Moreover, the practicability of IHAOHHO is also highlighted by four industrial engineering design problems. Compared with the original AO and HHO and five state-of-the-art algorithms, the results show that IHAOHHO has strong superior performance and promising prospects.

ACS Style

Shuang Wang; Heming Jia; Laith Abualigah; Qingxin Liu; Rong Zheng. An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems. Processes 2021, 9, 1551 .

AMA Style

Shuang Wang, Heming Jia, Laith Abualigah, Qingxin Liu, Rong Zheng. An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems. Processes. 2021; 9 (9):1551.

Chicago/Turabian Style

Shuang Wang; Heming Jia; Laith Abualigah; Qingxin Liu; Rong Zheng. 2021. "An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems." Processes 9, no. 9: 1551.

Journal article
Published: 30 July 2021 in Knowledge-Based Systems
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A recent meta-heuristic algorithm called Marine Predators Algorithm (MPA) is enhanced using Opposition-Based Learning (OBL) termed MPA-OBL to improve their search efficiency and convergence. A comprehensive set of experiments are performed to evaluate the MPA-OBL and prove the impact influence of merging OBL strategy with the original MPA in enhancing the quality of the solutions and the acceleration of the convergence speed, using IEEE CEC’2020 benchmark problems as recently complex optimization benchmark. In order to evaluate the performance of the proposed MPA-OBL, the effectiveness of conjunction of OBL with the original MPA and the other counterparts are calculated and compared with LSHADE with semi-parameter adaptation hybrid with CMA-ES (LSHADE_SPACMA-OBL), Restart covariance matrix adaptation ES (CMA_ES-OBL), Differential evolution (DE-OBL), Harris hawk optimization (HHO-OBL), Sine cosine algorithm (SCA-OBL), Salp swarm algorithm (SSA-OBL), and the original MPA. The extensive results and comparisons in terms of optimization metrics have revealed the superiority of the proposed MPA-OBL in solving the IEEE CEC’2020 benchmark problems and improving the convergence speed. Moreover, as a sequel to the proposed MPA-OBL, also, we have conducted experiments using two objective functions of Otsu and Kapur’s methods over a variety of benchmark images at different level of thresholds based on three commonly evaluation matrices namely Peak signal-to-noise ratio (PSNR), Structural similarity (SSIM), and Feature similarity (FSIM) indices are analyzed qualitatively and quantitatively. Eventually, the statistical post-hoc analysis reveal that the MPA-OBL obtains highly efficient and reliable results in comparison with the other competitor algorithms.

ACS Style

Essam H. Houssein; Kashif Hussain; Laith Abualigah; Mohamed Abd Elaziz; Waleed Alomoush; Gaurav Dhiman; Youcef Djenouri; Erik Cuevas. An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation. Knowledge-Based Systems 2021, 229, 107348 .

AMA Style

Essam H. Houssein, Kashif Hussain, Laith Abualigah, Mohamed Abd Elaziz, Waleed Alomoush, Gaurav Dhiman, Youcef Djenouri, Erik Cuevas. An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation. Knowledge-Based Systems. 2021; 229 ():107348.

Chicago/Turabian Style

Essam H. Houssein; Kashif Hussain; Laith Abualigah; Mohamed Abd Elaziz; Waleed Alomoush; Gaurav Dhiman; Youcef Djenouri; Erik Cuevas. 2021. "An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation." Knowledge-Based Systems 229, no. : 107348.

Review
Published: 13 July 2021 in Electronics
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Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

ACS Style

Waheeb Abu-Ulbeh; Maryam Altalhi; Laith Abualigah; Abdulwahab Almazroi; Putra Sumari; Amir Gandomi. Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations. Electronics 2021, 10, 1670 .

AMA Style

Waheeb Abu-Ulbeh, Maryam Altalhi, Laith Abualigah, Abdulwahab Almazroi, Putra Sumari, Amir Gandomi. Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations. Electronics. 2021; 10 (14):1670.

Chicago/Turabian Style

Waheeb Abu-Ulbeh; Maryam Altalhi; Laith Abualigah; Abdulwahab Almazroi; Putra Sumari; Amir Gandomi. 2021. "Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations." Electronics 10, no. 14: 1670.

Journal article
Published: 02 July 2021 in Processes
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One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

ACS Style

Laith Abualigah; Ali Diabat; Putra Sumari; Amir Gandomi. A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images. Processes 2021, 9, 1155 .

AMA Style

Laith Abualigah, Ali Diabat, Putra Sumari, Amir Gandomi. A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images. Processes. 2021; 9 (7):1155.

Chicago/Turabian Style

Laith Abualigah; Ali Diabat; Putra Sumari; Amir Gandomi. 2021. "A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images." Processes 9, no. 7: 1155.

Article
Published: 29 June 2021 in Multimedia Tools and Applications
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With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26 % enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

ACS Style

Ahmad Ziadeh; Laith Abualigah; Mohamed Abd Elaziz; Canan Batur Şahin; Abdulwahab Ali Almazroi; Mahmoud Omari. Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes. Multimedia Tools and Applications 2021, 1 -29.

AMA Style

Ahmad Ziadeh, Laith Abualigah, Mohamed Abd Elaziz, Canan Batur Şahin, Abdulwahab Ali Almazroi, Mahmoud Omari. Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes. Multimedia Tools and Applications. 2021; ():1-29.

Chicago/Turabian Style

Ahmad Ziadeh; Laith Abualigah; Mohamed Abd Elaziz; Canan Batur Şahin; Abdulwahab Ali Almazroi; Mahmoud Omari. 2021. "Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes." Multimedia Tools and Applications , no. : 1-29.

Journal article
Published: 02 June 2021 in IEEE Access
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In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different areas, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist non-dominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SP), Inverse Generalized Distance (IGD), Runtime (RT), and Generalized Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems.

ACS Style

Manoharan Premkumar; Pradeep Jangir; Balan Santhosh Kumar; Ravichandran Sowmya; Hassan Haes Alhelou; Laith Abualigah; Ali Riza Yildiz; SeyedAli Mirjalili. A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations. IEEE Access 2021, 9, 84263 -84295.

AMA Style

Manoharan Premkumar, Pradeep Jangir, Balan Santhosh Kumar, Ravichandran Sowmya, Hassan Haes Alhelou, Laith Abualigah, Ali Riza Yildiz, SeyedAli Mirjalili. A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations. IEEE Access. 2021; 9 (99):84263-84295.

Chicago/Turabian Style

Manoharan Premkumar; Pradeep Jangir; Balan Santhosh Kumar; Ravichandran Sowmya; Hassan Haes Alhelou; Laith Abualigah; Ali Riza Yildiz; SeyedAli Mirjalili. 2021. "A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations." IEEE Access 9, no. 99: 84263-84295.

Journal article
Published: 01 June 2021 in Electronics
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Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.

ACS Style

Hong Fan; Wu Du; Abdelghani Dahou; Ahmed Ewees; Dalia Yousri; Mohamed Elaziz; Ammar Elsheikh; Laith Abualigah; Mohammed Al-Qaness. Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit. Electronics 2021, 10, 1332 .

AMA Style

Hong Fan, Wu Du, Abdelghani Dahou, Ahmed Ewees, Dalia Yousri, Mohamed Elaziz, Ammar Elsheikh, Laith Abualigah, Mohammed Al-Qaness. Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit. Electronics. 2021; 10 (11):1332.

Chicago/Turabian Style

Hong Fan; Wu Du; Abdelghani Dahou; Ahmed Ewees; Dalia Yousri; Mohamed Elaziz; Ammar Elsheikh; Laith Abualigah; Mohammed Al-Qaness. 2021. "Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit." Electronics 10, no. 11: 1332.

Article
Published: 31 May 2021 in The Journal of Supercomputing
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The central cloud facilities based on virtual machines offer many benefits to reduce the scheduling costs and improve service availability and accessibility. The approach of cloud computing is practical due to the combination of security features and online services. In the tasks transfer, the source and target domains have differing feature spaces. This challenge becomes more complicated in network traffic, which leads to data transfer delay, and some critical tasks could not deliver at the right time. This paper proposes an efficient optimization method for task scheduling based on a hybrid multi-verse optimizer with a genetic algorithm called MVO-GA. The proposed MVO-GA is proposed to enhance the performance of tasks transfer via the cloud network based on cloud resources' workload. It is necessary to provide adequate transfer decisions to reschedule the transfer tasks based on the gathered tasks' efficiency weight in the cloud. The proposed method (MVO-GA) works on multiple properties of cloud resources: speed, capacity, task size, number of tasks, number of virtual machines, and throughput. The proposed method successfully optimizes the task scheduling of a large number of tasks (i.e., 1000–2000). The proposed MVO-GA got promising results in optimizing the large cloud tasks' transfer time, which reflects its effectiveness. The proposed method is evaluated based on using the simulation environment of the cloud using MATLAB distrusted system.

ACS Style

Laith Abualigah; Muhammad Alkhrabsheh. Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. The Journal of Supercomputing 2021, 1 -26.

AMA Style

Laith Abualigah, Muhammad Alkhrabsheh. Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. The Journal of Supercomputing. 2021; ():1-26.

Chicago/Turabian Style

Laith Abualigah; Muhammad Alkhrabsheh. 2021. "Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing." The Journal of Supercomputing , no. : 1-26.

Article
Published: 27 May 2021 in Cluster Computing
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Effective task scheduling is recognized as one of the main critical challenges in cloud computing; it is an essential step for effectively exploiting cloud computing resources, as several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and maximizing resource utilization. Task scheduling is an NP-hard problem, and consequently, finding the best solution may be difficult, particularly for Big Data applications. This paper presents an intelligent Big Data task scheduling approach for IoT cloud computing applications using a hybrid Dragonfly Algorithm. The Dragonfly algorithm is a newly introduced optimization algorithm for solving optimization problems which mimics the swarming behaviors of dragonflies. Our algorithm, MHDA, aims to decrease the makespan and increase resource utilization, and is thus a multi-objective approach. β-hill climbing is utilized as a local exploratory search to enhance the Dragonfly Algorithm’s exploitation ability and avoid being trapped in local optima. Two experimental studies were conducted on synthetic and real trace datasets using the CloudSim toolkit to compare MHDA to other well-known algorithms for solving task scheduling problems. The analysis, which included the use of a t-test, revealed that MHDA outperformed other well-known algorithms: MHDA converged faster than other methods, making it useful for Big Data task scheduling applications, and it achieved 17.12% improvement in the results.

ACS Style

Laith Abualigah; Ali Diabat; Mohamed Abd Elaziz. Intelligent workflow scheduling for Big Data applications in IoT cloud computing environments. Cluster Computing 2021, 1 -20.

AMA Style

Laith Abualigah, Ali Diabat, Mohamed Abd Elaziz. Intelligent workflow scheduling for Big Data applications in IoT cloud computing environments. Cluster Computing. 2021; ():1-20.

Chicago/Turabian Style

Laith Abualigah; Ali Diabat; Mohamed Abd Elaziz. 2021. "Intelligent workflow scheduling for Big Data applications in IoT cloud computing environments." Cluster Computing , no. : 1-20.

Journal article
Published: 24 May 2021 in Future Generation Computer Systems
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Cloud-fog computing frameworks are emerging paradigms developed to add benefits to the current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a key role, and the optimized schedule of IoT task requests can improve system performance and productivity. In this paper, we developed an alternative task scheduling technique for IoT requests in a cloud-fog environment based on a modified artificial ecosystem-based optimization (AEO), called AEOSSA. This modification is developed using the operators of the Salp Swarm Algorithm (SSA) in an attempt to enhance the exploitation ability of AEO during the process of finding the optimal solution for the problem under consideration. The performance of the designed AEOSSA approach to tackling the task scheduling problem is evaluated using different synthetic and real-world datasets of different sizes. In addition, a comparison is conducted between AEOSSA and other well-known metaheuristic methods for performance investigation. The experimental results demonstrate the high ability of AEOSSA to tackle the task scheduling problem and perform better than other methods according to the performance metrics such as makespan time and throughput.

ACS Style

Mohamed Abd Elaziz; Laith Abualigah; Ibrahim Attiya. Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Future Generation Computer Systems 2021, 124, 142 -154.

AMA Style

Mohamed Abd Elaziz, Laith Abualigah, Ibrahim Attiya. Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Future Generation Computer Systems. 2021; 124 ():142-154.

Chicago/Turabian Style

Mohamed Abd Elaziz; Laith Abualigah; Ibrahim Attiya. 2021. "Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments." Future Generation Computer Systems 124, no. : 142-154.

Journal article
Published: 19 May 2021 in Expert Systems with Applications
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In this paper, an Improved version of the Slime Mould Algorithm (ISMA) is proposed and applied to efficiently solve the single-and bi-objective Economic and Emission Dispatch (EED) problems considering valve point effect. ISMA is developed to improve the performance of the conventional Slime Mould Algorithm (SMA). In ISMA, the solution positions are updated depending on two equations borrowed from the sine–cosine algorithm (SCA) to obtain the best solution. Multi-objective SMA (MOSMA) and Multi-objective ISMA (MOISMA) are developed based on the Pareto dominance concept and fuzzy decision-making. In the multi-objective EED problem, MOSMA and MOISMA are applied to minimize the total fuel costs and total emission with the valve point effect simultaneously. The proposed single-and bi-objective economic emission dispatch algorithms are validated using five test systems, 6-units, 10-units, 11-units, 40-units, and 110-units. The performance of the proposed algorithm is compared with Harris Hawk Optimizer (HHO), Jellyfish Search optimizer (JS), Tunicate Swarm Algorithm (TSA), Particle swarm optimization (PSO), and SMA algorithms. The results show that the proposed algorithms are more robust than other well-known algorithms. Feasible solutions using the proposed algorithms are also achieved, which adjust the schedule of generation without violation of the operating generation limits.

ACS Style

Mohamed H. Hassan; Salah Kamel; Laith Abualigah; Ahmad Eid. Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Systems with Applications 2021, 182, 115205 .

AMA Style

Mohamed H. Hassan, Salah Kamel, Laith Abualigah, Ahmad Eid. Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Systems with Applications. 2021; 182 ():115205.

Chicago/Turabian Style

Mohamed H. Hassan; Salah Kamel; Laith Abualigah; Ahmad Eid. 2021. "Development and application of slime mould algorithm for optimal economic emission dispatch." Expert Systems with Applications 182, no. : 115205.

Original article
Published: 12 May 2021 in Neural Computing and Applications
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The principal motivation of the marine predators algorithm (MPA) is the common foraging technique, including Lévy and Brownian motions in ocean predators coupled with optimal contact intensity policy in predator–prey biological interaction. This paper proposes an improved marine predators algorithm (IMPA), which is an extension of the original MPA. The suggested improvements lead to rapid convergence and avoid local minima stagnation for the original MPA. IMPA controls the active and reactive power injected into distribution systems to minimize the total system losses and the total voltage deviations and maximize the voltage stability and improve the distribution system's overall performance. On the one hand, the proposed IMPA determines the optimal location and active power (location and size, respectively) of distributed generation (DG). On the other hand, the IMPA controls reactive power by optimally placing and sizing the shunt capacitors (SCs) and determining the PF of DGs. Two standard test systems, 69-bus and 118-bus distribution networks, are considered to prove the proposed algorithm’s efficiency and scalability. Results of the proposed IMPA are compared with those obtained by MPA, AEO, and PSO algorithms. The findings of the simulation results demonstrate that the proposed IMPA can effectively find the optimal problem solutions and beats the other algorithms. Moreover, the framework of multi-objective IMPA outperforms based on MPA in terms of the performance measures of diversity, spacing, coverage, and hypervolume.

ACS Style

Ahmad Eid; Salah Kamel; Laith Abualigah. Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Computing and Applications 2021, 1 -29.

AMA Style

Ahmad Eid, Salah Kamel, Laith Abualigah. Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Computing and Applications. 2021; ():1-29.

Chicago/Turabian Style

Ahmad Eid; Salah Kamel; Laith Abualigah. 2021. "Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks." Neural Computing and Applications , no. : 1-29.

Original article
Published: 01 May 2021 in Neural Computing and Applications
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The automatic detection of software vulnerabilities is considered a complex and common research problem. It is possible to detect several security vulnerabilities using static analysis (SA) tools, but comparatively high false-positive rates are observed in this case. Existing solutions to this problem depend on human experts to identify functionality, and as a result, several vulnerabilities are often overlooked. This paper introduces a novel approach for effectively and reliably finding vulnerabilities in open-source software programs. In this paper, we are motivated to examine the potential of the clonal selection theory. A novel deep learning-based vulnerability detection model is proposed to define features using the clustering theory of the clonal selection algorithm. To our knowledge, this is the first time we have used deep-learned long-lived team-hacker features to process memories of sequential features and mapping from the entire history of previous inputs to target vectors in theory. With an immune-based feature selection model, the proposed approach aimed to improve static analyses' detection abilities. A real-world SA dataset is used based on three open-source PHP applications. Comparisons are conducted based on using a classification model for all features to measure the proposed feature selection methods' classification improvement. The results demonstrated that the proposed method got significant enhancements, which occurred in the classification accuracy also in the true positive rate.

ACS Style

Canan Batur Şahin; Laith Abualigah. A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection. Neural Computing and Applications 2021, 1 -19.

AMA Style

Canan Batur Şahin, Laith Abualigah. A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection. Neural Computing and Applications. 2021; ():1-19.

Chicago/Turabian Style

Canan Batur Şahin; Laith Abualigah. 2021. "A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection." Neural Computing and Applications , no. : 1-19.

Original paper
Published: 19 April 2021 in Archives of Computational Methods in Engineering
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The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter spaces. There is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. Nowadays, there is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. This review article demonstrates the applications of different metaheuristic algorithms in enhancing the design process of different mechanical systems. First, the basic concepts of common used metaheuristic algorithms are introduced. Then the applications of theses algorithms in optimization of different mechanical systems are discussed.

ACS Style

Mohamed Abd Elaziz; Ammar H. Elsheikh; Diego Oliva; Laith Abualigah; Songfeng Lu; Ahmed A. Ewees. Advanced Metaheuristic Techniques for Mechanical Design Problems: Review. Archives of Computational Methods in Engineering 2021, 1 -22.

AMA Style

Mohamed Abd Elaziz, Ammar H. Elsheikh, Diego Oliva, Laith Abualigah, Songfeng Lu, Ahmed A. Ewees. Advanced Metaheuristic Techniques for Mechanical Design Problems: Review. Archives of Computational Methods in Engineering. 2021; ():1-22.

Chicago/Turabian Style

Mohamed Abd Elaziz; Ammar H. Elsheikh; Diego Oliva; Laith Abualigah; Songfeng Lu; Ahmed A. Ewees. 2021. "Advanced Metaheuristic Techniques for Mechanical Design Problems: Review." Archives of Computational Methods in Engineering , no. : 1-22.

Review
Published: 18 April 2021 in Neural Computing and Applications
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Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified.

ACS Style

Mohamed Abd Elaziz; Abdelghani Dahou; Laith Abualigah; Liyang Yu; Mohammad Alshinwan; Ahmad M. Khasawneh; Songfeng Lu. Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Computing and Applications 2021, 1 -21.

AMA Style

Mohamed Abd Elaziz, Abdelghani Dahou, Laith Abualigah, Liyang Yu, Mohammad Alshinwan, Ahmad M. Khasawneh, Songfeng Lu. Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Computing and Applications. 2021; ():1-21.

Chicago/Turabian Style

Mohamed Abd Elaziz; Abdelghani Dahou; Laith Abualigah; Liyang Yu; Mohammad Alshinwan; Ahmad M. Khasawneh; Songfeng Lu. 2021. "Advanced metaheuristic optimization techniques in applications of deep neural networks: a review." Neural Computing and Applications , no. : 1-21.

Journal article
Published: 18 April 2021 in Applied Soft Computing
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The capacitated vehicle routing problem (CVRP) is a classical combinatorial optimization problem, which has received much attention due to its main challenges as distribution, logistics, and transportation. This proposed attempts to find the vehicle routes with minimizing traveling distance, in which the excellent solution delivers a set of customers in one visit by capacitated vehicle. For solving the CVRP problem, a cooperative hybrid firefly algorithm (CVRP-CHFA) is proposed in this paper with multiple firefly algorithm (FA) populations. Each FA is hybridized with two types of local search (i.e., Improved 2-opt as a local search and 2-h-opt as a mutation operator) and genetic operators. The proposed algorithms (FAs) communicate from time to time for exchanging some solutions (fireflies). The main aim of the hybridization and communication strategies is to maintain the diversity of populations to prevent the proposed algorithm from falling into local optima and overcome the drawbacks of a single swarm FA. The experiments are conducted on 108 instances from eight standard benchmarks. The results revealed that the proposed CVRP-CHFA got promising results compared to other well-known methods. Moreover, the proposed CVRP-CHFA significantly outperformed the recent three hybrid firefly algorithms.

ACS Style

Asma M. Altabeeb; Abdulqader M. Mohsen; Laith Abualigah; Abdullatif Ghallab. Solving capacitated vehicle routing problem using cooperative firefly algorithm. Applied Soft Computing 2021, 108, 107403 .

AMA Style

Asma M. Altabeeb, Abdulqader M. Mohsen, Laith Abualigah, Abdullatif Ghallab. Solving capacitated vehicle routing problem using cooperative firefly algorithm. Applied Soft Computing. 2021; 108 ():107403.

Chicago/Turabian Style

Asma M. Altabeeb; Abdulqader M. Mohsen; Laith Abualigah; Abdullatif Ghallab. 2021. "Solving capacitated vehicle routing problem using cooperative firefly algorithm." Applied Soft Computing 108, no. : 107403.

Article
Published: 31 March 2021 in Applied Intelligence
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The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

ACS Style

Canan Batur Şahin; Özlem Batur Dinler; Laith Abualigah. Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Applied Intelligence 2021, 1 -17.

AMA Style

Canan Batur Şahin, Özlem Batur Dinler, Laith Abualigah. Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Applied Intelligence. 2021; ():1-17.

Chicago/Turabian Style

Canan Batur Şahin; Özlem Batur Dinler; Laith Abualigah. 2021. "Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features." Applied Intelligence , no. : 1-17.

Original article
Published: 31 March 2021 in Engineering with Computers
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Feature selection (FS) methods are necessary to develop intelligent analysis tools that require data preprocessing and enhancing the performance of the machine learning algorithms. FS aims to maximize the classification accuracy by minimizing the number of selected features. This paper presents a new FS method using a modified Slime mould algorithm (SMA) based on the firefly algorithm (FA). In the developed SMAFA, FA is adopted to improve the exploration of SMA, since it has high ability to discover the feasible regions which have optima solution. This will lead to enhance the convergence by increasing the quality of the final output. SMAFA is evaluated using twenty UCI datasets and also with comprehensive comparisons to a number of the existing MH algorithms. To further assess the applicability of SMAFA, two high-dimensional datasets related to the QSAR modeling are used. Experimental results verified the promising performance of SMAFA using different performance measures.

ACS Style

Ahmed A. Ewees; Laith Abualigah; Dalia Yousri; Zakariya Yahya Algamal; Mohammed A. A. Al-Qaness; Rehab Ali Ibrahim; Mohamed Abd Elaziz. Improved Slime Mould Algorithm based on Firefly Algorithm for feature selection: A case study on QSAR model. Engineering with Computers 2021, 1 -15.

AMA Style

Ahmed A. Ewees, Laith Abualigah, Dalia Yousri, Zakariya Yahya Algamal, Mohammed A. A. Al-Qaness, Rehab Ali Ibrahim, Mohamed Abd Elaziz. Improved Slime Mould Algorithm based on Firefly Algorithm for feature selection: A case study on QSAR model. Engineering with Computers. 2021; ():1-15.

Chicago/Turabian Style

Ahmed A. Ewees; Laith Abualigah; Dalia Yousri; Zakariya Yahya Algamal; Mohammed A. A. Al-Qaness; Rehab Ali Ibrahim; Mohamed Abd Elaziz. 2021. "Improved Slime Mould Algorithm based on Firefly Algorithm for feature selection: A case study on QSAR model." Engineering with Computers , no. : 1-15.

Journal article
Published: 23 March 2021 in Computers & Industrial Engineering
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This paper proposes a novel population-based optimization method, called Aquila Optimizer (AO), which is inspired by the Aquila’s behaviors in nature during the process of catching the prey. Hence, the optimization procedures of the proposed AO algorithm are represented in four methods; selecting the search space by high soar with the vertical stoop, exploring within a diverge search space by contour flight with short glide attack, exploiting within a converge search space by low flight with slow descent attack, and swooping by walk and grab prey. To validate the new optimizer’s ability to find the optimal solution for different optimization problems, a set of experimental series is conducted. For example, during the first experiment, AO is applied to find the solution of well-known 23 functions. The second and third experimental series aims to evaluate the AO’s performance to find solutions for more complex problems such as thirty CEC2017 test functions and ten CEC2019 test functions, respectively. Finally, a set of seven real-world engineering problems are used. From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed. Matlab codes of AO are available at https://www.mathworks.com/matlabcentral/fileexchange/89381-aquila-optimizer-a-meta-heuristic-optimization-algorithm and Java codes are available at https://www.mathworks.com/matlabcentral/fileexchange/89386-aquila-optimizer-a-meta-heuristic-optimization-algorithm.

ACS Style

Laith Abualigah; Dalia Yousri; Mohamed Abd Elaziz; Ahmed A. Ewees; Mohammed A.A. Al-Qaness; Amir H. Gandomi. Aquila Optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering 2021, 157, 107250 .

AMA Style

Laith Abualigah, Dalia Yousri, Mohamed Abd Elaziz, Ahmed A. Ewees, Mohammed A.A. Al-Qaness, Amir H. Gandomi. Aquila Optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering. 2021; 157 ():107250.

Chicago/Turabian Style

Laith Abualigah; Dalia Yousri; Mohamed Abd Elaziz; Ahmed A. Ewees; Mohammed A.A. Al-Qaness; Amir H. Gandomi. 2021. "Aquila Optimizer: A novel meta-heuristic optimization algorithm." Computers & Industrial Engineering 157, no. : 107250.

Article
Published: 22 February 2021 in Cluster Computing
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Feature selection (FS) is a real-world problem that can be solved using optimization techniques. These techniques proposed solutions to make a predictive model, which minimizes the classifier's prediction errors by selecting informative or important features by discarding redundant, noisy, and irrelevant attributes in the original dataset. A new hybrid feature selection method is proposed using the Sine Cosine Algorithm (SCA) and Genetic Algorithm (GA), called SCAGA. Typically, optimization methods have two main search strategies; exploration of the search space and exploitation to determine the optimal solution. The proposed SCAGA resulted in better performance when balancing between exploitation and exploration strategies of the search space. The proposed SCAGA has also been evaluated using the following evaluation criteria: classification accuracy, worst fitness, mean fitness, best fitness, the average number of features, and standard deviation. Moreover, the maximum accuracy of a classification and the minimal features were obtained in the results. The results were also compared with a basic Sine Cosine Algorithm (SCA) and other related approaches published in literature such as Ant Lion Optimization and Particle Swarm Optimization. The comparison showed that the obtained results from the SCAGA method were the best overall the tested datasets from the UCI machine learning repository.

ACS Style

Laith Abualigah; Akram Jamal Dulaimi. A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm. Cluster Computing 2021, 1 -16.

AMA Style

Laith Abualigah, Akram Jamal Dulaimi. A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm. Cluster Computing. 2021; ():1-16.

Chicago/Turabian Style

Laith Abualigah; Akram Jamal Dulaimi. 2021. "A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm." Cluster Computing , no. : 1-16.