Khaled H. Almotairi - Academia.edu (original) (raw)

Papers by Khaled H. Almotairi

Applied Intelligence , 2022

This paper studied the multilevel threshold image segmentation-based metaheuristics optimization ... more This paper studied the multilevel threshold image segmentation-based metaheuristics optimization methods and their applications. Image segmentation is a common problem in the image processing domain, and it is an essential process in image analysis, directly impacting image analysis results. Thresholding is one of the most manageable and extensively utilized methods for handling image segmentation problems. In this paper, four main parts are given; (1) We present the main procedures and definitions of the multilevel threshold image segmentation problem. The standard fitness function and the evaluation criteria are also given to facilitate the problem representation for the new researchers in this domain. (2) All the related works that have used optimization methods in solving the multilevel threshold image segmentation problems are presented in more detail, focusing on the image segmentation problem and its solutions. The given related works are outlined according to the used algorithms. (3) Comprehensive results and analysis of several well-known optimization algorithms are conducted to solve the multilevel threshold image segmentation problems. These comparative methods include Aquila Optimizer (AO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimizer (PSO), Marine Predators Algorithm (MPA), Krill Herd Algorithm (KHA), Multi-verse Optimizer (MVO), and Gray Wolf Optimizer (GWO). Eight standard benchmark images are used to test the comparative methods. The results are evaluated using three standard measures: fitness function, PeakSignal-to-Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). (4) Discussion, open challenging, and new trends are given to help the scholars in future research get near the common problems and defect in that domain. The collected data in this review has been taken from google scholar using the stander search method. The main keywords that have been used in the search are multilevel, threshold, image, segmentation, optimization, and algorithm. We covered all the published papers in detail according to the given information, focusing on finding the common problems that still need further investigation. Furthermore, future research directions based on recently evolving designs are outlined, which should undoubtedly aid current researchers and practitioners and pave the way for new researchers interested in multilevel threshold image segmentation to seek their research in the field.

Big Data and Cognitive Computing

Integrating machine learning technologies into artificial intelligence (AI) is at the forefront o... more Integrating machine learning technologies into artificial intelligence (AI) is at the forefront of the scientific and technological tools employed to combat the COVID-19 pandemic. This study assesses different uses and deployments of modern technology for combating the COVID-19 pandemic at various levels, such as image processing, tracking of disease, prediction of outcomes, and computational medicine. The results prove that computerized tomography (CT) scans help to diagnose patients infected by COVID-19. This includes two-sided, multilobar ground glass opacification (GGO) by a posterior distribution or peripheral, primarily in the lower lobes, and fewer recurrences in the intermediate lobe. An extensive search of modern technology databases relating to COVID-19 was undertaken. Subsequently, a review of the extracted information from the database search looked at how technology can be employed to tackle the pandemic. We discussed the technological advancements deployed to alleviate...

Journal of Umm Al-Qura University for Engineering and Architecture

Internet of things (IoT) enables machine-to-machine, human-to-human and machine-to-human interact... more Internet of things (IoT) enables machine-to-machine, human-to-human and machine-to-human interaction. Recent advancement in IoT systems has positively impacted the daily activities of humans, from accessing information to the delivery of service in real-time. This has improved healthcare management and services, especially in medical hospitals, for effective and timely access to diagnostic information and treatment of patients. Several existing research mainly focused on the design of IoT architecture, its challenges, and benefits to human society with minor or without considering applying IoT in the healthcare domain. To bridge this gap, this study investigates the implications of IoT integration in the healthcare management domain. It presents a detailed discussion on IoT utilization to improve the functionalities of hospital management system. It also discusses some potential emerging innovations that aids the development and application of IoT in hospital management systems. Inv...

Journal of Healthcare Engineering

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most c... more Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist’s workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the K...

Applied Sciences

The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. ... more The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback gorillas and competing with silverback gorillas for females. However, like other Metaheuristic Algorithms, the GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order to improve the performance of the GTO, this paper proposes a modified Gorilla Troops Optimizer (MGTO). The improvement strategies include three parts: Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS), Teaching–Learning-Based Optimization (TLBO) and Quasi-Reflection-Based Learning (QRBL). Firstly, QIBAS is utilized to enhance the diversity of the position of the silverback. Secondly, the teacher phase of TLBO is introduced to the update the behavior of following the silverba...

PLOS ONE

A vehicular network offers diverse beneficial services related to video streaming in different ty... more A vehicular network offers diverse beneficial services related to video streaming in different types of setups, including rural and urban. Some of the recent issues in vehicular communication include prospect of leveraging machine learning and blockchain for privacy and security enhancement, and resource allocation for video streaming coupled with integration of 6G networks for high data rate. Considering the extreme mobility and dynamic structure of vehicular networks and the high data rates of video streams, a unitary route may not support the required quality of a video stream. To achieve load balancing, connectivity among vehicles, path diversity, and low delay, the multipath transmission with a delay-tolerant network (DTN) concept based on a node disjoint algorithm is considered. In this proposed study, video frames are categorized in accordance with priority and forwarded via two graded paths. The first path carries the video reference frame, which is the most important frame ...

In the past decade, the development of wireless communication technologies has made the use of th... more In the past decade, the development of wireless communication technologies has made the use of the Internet ubiquitous. With the increasing number of new inventions and applications using wireless communication, more interference is introduced among wireless devices that results in limiting the capacity of wireless networks. Many approaches have been proposed to improve the capacity. One approach is to exploit multiple channels by allowing concurrent transmissions, and therefore it can provide high capacity. Many available, license-exempt, and nonoverlapping channels are the main advantages of using this approach. Another approach that increases the network capacity is to adjust the transmission power; hence, it reduces interference among devices and increases the spatial reuse. Integrating both approaches provides further capacity. However, without careful transmission power control (TPC) design, the network performance is limited. The first part of this thesis tackles the integrat...

PLoS ONE, 2022

A vehicular network offers diverse beneficial services related to video streaming in different ty... more A vehicular network offers diverse beneficial services related to video streaming in different types of setups, including rural and urban. Some of the recent issues in vehicular communication include prospect of leveraging machine learning and blockchain for privacy and security enhancement, and resource allocation for video streaming coupled with integration of 6G networks for high data rate. Considering the extreme mobility and dynamic structure of vehicular networks and the high data rates of video streams, a unitary route may not support the required quality of a video stream. To achieve load balancing, connectivity among vehicles, path diversity, and low delay, the multipath transmission with a delay-tolerant network (DTN) concept based on a node disjoint algorithm is considered. In this proposed study, video frames are categorized in accordance with priority and forwarded via two graded paths. The first path carries the video reference frame, which is the most important frame for video decoding. The second path carries neighboring frames during video transmission. For the efficient selection of an optimal relay vehicle, a communication cost function is introduced into the existing DTN. This communication cost function is based on three key enhancement parameters: link stability rate, accessible bandwidth estimation, and transmission delay. The improvement in this study, is the integration of store-carry-forward strategy to the existing multipath data forwarding strategy. On the basis of the simulation outcomes, the proposed multipath video data communication in a vehicular DTN (MVDTN) scheme can enhance video data delivery in terms of packet loss ratio, end-to-end delay, structural similarity index measure, and peak signal-to-noise ratio. Considering the aforementioned metrics, our proposed schemes outperform the baseline schemes, namely, road-based multi-metrics forwarder selection evaluation for multipath video streaming and quality of service-aware multipath video streaming for an urban vehicular ad hoc network by using ant colony optimization.

Neural Computing and Applications, Aug 30, 2022

Neural Computing and Applications

Journal of Intelligent & Fuzzy Systems

One of the fastest-growing fields in today’s world is data analytics. Data analytics paved the wa... more One of the fastest-growing fields in today’s world is data analytics. Data analytics paved the way for a significant number of research and development in various fields including medicine and vaccine development, DNA analysis, artificial intelligence and many more. Data plays a very important role in providing the required results and helps in making critical decisions and predictions. However, ethical and legislative restrictions sometimes make it difficult for scientists to acquire data. For example, during the COVID-19 pandemic, data was very limited due to privacy and regulatory issues. To address data unavailability, data scientists usually leverage machine learning algorithms such as Generative Adversarial Networks (GAN) to augment data from existing samples. Today, there are over 450 algorithms that are designed to re-generate or augment data in case of unavailability of the data. With many algorithms in the market, it is practically impossible to predict which algorithm bes...

Electronics

Emotional intelligence is the automatic detection of human emotions using various intelligent met... more Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest...

Engineering designs are common industrial optimization problems that need an efficient method to ... more Engineering designs are common industrial optimization problems that need an efficient method to determine the parameters of the problems. This paper proposes a novel engineering design parameters identification method based on an enhanced optimization method called IRSA. The conventional reptile search algorithm (RSA) is utilized in the proposed IRSA method with the mutation technique (MT). These two search methods are used to find the optimal parameters values for the given problems and are employed based on a novel mean transition mechanism . The proposed mean transition mechanism adjusts the searching process by changing between the search process (i.e., RSA or MT) to avoid the main weaknesses of the original RSA: the permutation convergence and unbalance between the search methods. Experiments are conducted on ten benchmark functions from CEC2019 and five industrial engineering design problems. The results are evaluated using worst, mean, and best fitness function values. The proposed method is compared with other well-established methods, and it got better and promising results. The proposed IRSA method’s performance proved its ability to address the mathematical benchmark functions and engineering design problems.

Journal of King Saud University - Computer and Information Sciences

Electronics

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates th... more The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

International Journal of Advanced and Applied Sciences, May 1, 2022

Symmetry

As data volumes have increased and difficulty in tackling vast and complicated problems has emerg... more As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering t...

Applied Intelligence , 2022

This paper studied the multilevel threshold image segmentation-based metaheuristics optimization ... more This paper studied the multilevel threshold image segmentation-based metaheuristics optimization methods and their applications. Image segmentation is a common problem in the image processing domain, and it is an essential process in image analysis, directly impacting image analysis results. Thresholding is one of the most manageable and extensively utilized methods for handling image segmentation problems. In this paper, four main parts are given; (1) We present the main procedures and definitions of the multilevel threshold image segmentation problem. The standard fitness function and the evaluation criteria are also given to facilitate the problem representation for the new researchers in this domain. (2) All the related works that have used optimization methods in solving the multilevel threshold image segmentation problems are presented in more detail, focusing on the image segmentation problem and its solutions. The given related works are outlined according to the used algorithms. (3) Comprehensive results and analysis of several well-known optimization algorithms are conducted to solve the multilevel threshold image segmentation problems. These comparative methods include Aquila Optimizer (AO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimizer (PSO), Marine Predators Algorithm (MPA), Krill Herd Algorithm (KHA), Multi-verse Optimizer (MVO), and Gray Wolf Optimizer (GWO). Eight standard benchmark images are used to test the comparative methods. The results are evaluated using three standard measures: fitness function, PeakSignal-to-Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). (4) Discussion, open challenging, and new trends are given to help the scholars in future research get near the common problems and defect in that domain. The collected data in this review has been taken from google scholar using the stander search method. The main keywords that have been used in the search are multilevel, threshold, image, segmentation, optimization, and algorithm. We covered all the published papers in detail according to the given information, focusing on finding the common problems that still need further investigation. Furthermore, future research directions based on recently evolving designs are outlined, which should undoubtedly aid current researchers and practitioners and pave the way for new researchers interested in multilevel threshold image segmentation to seek their research in the field.

Big Data and Cognitive Computing

Integrating machine learning technologies into artificial intelligence (AI) is at the forefront o... more Integrating machine learning technologies into artificial intelligence (AI) is at the forefront of the scientific and technological tools employed to combat the COVID-19 pandemic. This study assesses different uses and deployments of modern technology for combating the COVID-19 pandemic at various levels, such as image processing, tracking of disease, prediction of outcomes, and computational medicine. The results prove that computerized tomography (CT) scans help to diagnose patients infected by COVID-19. This includes two-sided, multilobar ground glass opacification (GGO) by a posterior distribution or peripheral, primarily in the lower lobes, and fewer recurrences in the intermediate lobe. An extensive search of modern technology databases relating to COVID-19 was undertaken. Subsequently, a review of the extracted information from the database search looked at how technology can be employed to tackle the pandemic. We discussed the technological advancements deployed to alleviate...

Journal of Umm Al-Qura University for Engineering and Architecture

Internet of things (IoT) enables machine-to-machine, human-to-human and machine-to-human interact... more Internet of things (IoT) enables machine-to-machine, human-to-human and machine-to-human interaction. Recent advancement in IoT systems has positively impacted the daily activities of humans, from accessing information to the delivery of service in real-time. This has improved healthcare management and services, especially in medical hospitals, for effective and timely access to diagnostic information and treatment of patients. Several existing research mainly focused on the design of IoT architecture, its challenges, and benefits to human society with minor or without considering applying IoT in the healthcare domain. To bridge this gap, this study investigates the implications of IoT integration in the healthcare management domain. It presents a detailed discussion on IoT utilization to improve the functionalities of hospital management system. It also discusses some potential emerging innovations that aids the development and application of IoT in hospital management systems. Inv...

Journal of Healthcare Engineering

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most c... more Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist’s workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the K...

Applied Sciences

The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. ... more The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback gorillas and competing with silverback gorillas for females. However, like other Metaheuristic Algorithms, the GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order to improve the performance of the GTO, this paper proposes a modified Gorilla Troops Optimizer (MGTO). The improvement strategies include three parts: Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS), Teaching–Learning-Based Optimization (TLBO) and Quasi-Reflection-Based Learning (QRBL). Firstly, QIBAS is utilized to enhance the diversity of the position of the silverback. Secondly, the teacher phase of TLBO is introduced to the update the behavior of following the silverba...

PLOS ONE

A vehicular network offers diverse beneficial services related to video streaming in different ty... more A vehicular network offers diverse beneficial services related to video streaming in different types of setups, including rural and urban. Some of the recent issues in vehicular communication include prospect of leveraging machine learning and blockchain for privacy and security enhancement, and resource allocation for video streaming coupled with integration of 6G networks for high data rate. Considering the extreme mobility and dynamic structure of vehicular networks and the high data rates of video streams, a unitary route may not support the required quality of a video stream. To achieve load balancing, connectivity among vehicles, path diversity, and low delay, the multipath transmission with a delay-tolerant network (DTN) concept based on a node disjoint algorithm is considered. In this proposed study, video frames are categorized in accordance with priority and forwarded via two graded paths. The first path carries the video reference frame, which is the most important frame ...

In the past decade, the development of wireless communication technologies has made the use of th... more In the past decade, the development of wireless communication technologies has made the use of the Internet ubiquitous. With the increasing number of new inventions and applications using wireless communication, more interference is introduced among wireless devices that results in limiting the capacity of wireless networks. Many approaches have been proposed to improve the capacity. One approach is to exploit multiple channels by allowing concurrent transmissions, and therefore it can provide high capacity. Many available, license-exempt, and nonoverlapping channels are the main advantages of using this approach. Another approach that increases the network capacity is to adjust the transmission power; hence, it reduces interference among devices and increases the spatial reuse. Integrating both approaches provides further capacity. However, without careful transmission power control (TPC) design, the network performance is limited. The first part of this thesis tackles the integrat...

PLoS ONE, 2022

A vehicular network offers diverse beneficial services related to video streaming in different ty... more A vehicular network offers diverse beneficial services related to video streaming in different types of setups, including rural and urban. Some of the recent issues in vehicular communication include prospect of leveraging machine learning and blockchain for privacy and security enhancement, and resource allocation for video streaming coupled with integration of 6G networks for high data rate. Considering the extreme mobility and dynamic structure of vehicular networks and the high data rates of video streams, a unitary route may not support the required quality of a video stream. To achieve load balancing, connectivity among vehicles, path diversity, and low delay, the multipath transmission with a delay-tolerant network (DTN) concept based on a node disjoint algorithm is considered. In this proposed study, video frames are categorized in accordance with priority and forwarded via two graded paths. The first path carries the video reference frame, which is the most important frame for video decoding. The second path carries neighboring frames during video transmission. For the efficient selection of an optimal relay vehicle, a communication cost function is introduced into the existing DTN. This communication cost function is based on three key enhancement parameters: link stability rate, accessible bandwidth estimation, and transmission delay. The improvement in this study, is the integration of store-carry-forward strategy to the existing multipath data forwarding strategy. On the basis of the simulation outcomes, the proposed multipath video data communication in a vehicular DTN (MVDTN) scheme can enhance video data delivery in terms of packet loss ratio, end-to-end delay, structural similarity index measure, and peak signal-to-noise ratio. Considering the aforementioned metrics, our proposed schemes outperform the baseline schemes, namely, road-based multi-metrics forwarder selection evaluation for multipath video streaming and quality of service-aware multipath video streaming for an urban vehicular ad hoc network by using ant colony optimization.

Neural Computing and Applications, Aug 30, 2022

Neural Computing and Applications

Journal of Intelligent & Fuzzy Systems

One of the fastest-growing fields in today’s world is data analytics. Data analytics paved the wa... more One of the fastest-growing fields in today’s world is data analytics. Data analytics paved the way for a significant number of research and development in various fields including medicine and vaccine development, DNA analysis, artificial intelligence and many more. Data plays a very important role in providing the required results and helps in making critical decisions and predictions. However, ethical and legislative restrictions sometimes make it difficult for scientists to acquire data. For example, during the COVID-19 pandemic, data was very limited due to privacy and regulatory issues. To address data unavailability, data scientists usually leverage machine learning algorithms such as Generative Adversarial Networks (GAN) to augment data from existing samples. Today, there are over 450 algorithms that are designed to re-generate or augment data in case of unavailability of the data. With many algorithms in the market, it is practically impossible to predict which algorithm bes...

Electronics

Emotional intelligence is the automatic detection of human emotions using various intelligent met... more Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest...

Engineering designs are common industrial optimization problems that need an efficient method to ... more Engineering designs are common industrial optimization problems that need an efficient method to determine the parameters of the problems. This paper proposes a novel engineering design parameters identification method based on an enhanced optimization method called IRSA. The conventional reptile search algorithm (RSA) is utilized in the proposed IRSA method with the mutation technique (MT). These two search methods are used to find the optimal parameters values for the given problems and are employed based on a novel mean transition mechanism . The proposed mean transition mechanism adjusts the searching process by changing between the search process (i.e., RSA or MT) to avoid the main weaknesses of the original RSA: the permutation convergence and unbalance between the search methods. Experiments are conducted on ten benchmark functions from CEC2019 and five industrial engineering design problems. The results are evaluated using worst, mean, and best fitness function values. The proposed method is compared with other well-established methods, and it got better and promising results. The proposed IRSA method’s performance proved its ability to address the mathematical benchmark functions and engineering design problems.

Journal of King Saud University - Computer and Information Sciences

Electronics

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates th... more The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

International Journal of Advanced and Applied Sciences, May 1, 2022

Symmetry

As data volumes have increased and difficulty in tackling vast and complicated problems has emerg... more As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering t...