Browsing by Author "Mirjalili, Seyedali"

Now showing 1 - 10 of 10

Binary whale optimization algorithm for topology planning in wireless mesh networks
(Elsevier, 2023) Taleb, Sylia Mekhmoukh; Meraihi, Yassine; Mirjalili, Seyedali; Yahia, Selma; Ramdane-Cherif, Amar
The objective of this research is to tackle the topology planning issue in Wireless Mesh Networks (WMNs) through the implementation of a Binary Whale Optimization Algorithm (BWOA). S-shaped and V-shaped families of transfer functions are employed to obtain a binary versions of WOA. BWOA is designed to reduce the number of mesh routers needed to meet the full coverage and full connectivity requirements. The performance of BWOA is evaluated using three metrics, namely the minimum, maximum, and average number of mesh routers, while taking into account variations in the number of mesh clients. According to the findings of the simulations carried out in Matlab®, BWOA algorithms utilizing V-shaped transfer functions outperform S-shaped transfer functions-based BWOA algorithms in terms of required number of mesh routers.
A comprehensive survey of sine cosine algorithm : variants and applications
(Springer, 2021) Benmessaoud Gabis, Asma; Meraihi, Yassine; Mirjalili, Seyedali; Ramdane‑Cherif, Amar
Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA
An Enhanced Aquila Optimizer Algorithm for Resource Allocation in Indoor Multi-user IoT VLC System
(2023) Yahia, Selma; Meraihi, Yassine; Mekhmoukh Taleb, Sylia; Mirjalili, Seyedali; Ramdane-Cherif, Amar; B. Eldeeb, Hossien; Muhaidat, Sami
Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and co- channel interferences, which can greatly improve network perfor- mance and user satisfaction. This paper focuses on an indoor IoT- based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.
An enhanced aquila-based resource allocation for efficient indoor IoT visible light communication
(Institute of Electrical and Electronics Engineers Inc., 2023) Yahia, Selma; Meraihi, Yassine; Taleb, Sylia Mekhmoukh; Mirjalili, Seyedali; Ramdane-Cherif, Amar; Ho, Tu Dac; Eldeeb, Hossien B.; Muhaidat, Sami
Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users' cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems
An enhanced whale optimization algorithm with opposition-based learning for LEDs placement in indoor VLC systems
(Elsevier, 2023) Benayad, Abdelbaki; Boustil, Amel; Meraihi, Yassine; Mirjalili, Seyedali; Yahia, Selma; Taleb, Sylia Mekhmoukh
Visible Light Communication (VLC) is a new technology that has attracted lately much interest from researchers and academics. It allows communication between users using photo-detectors (PDs) as receivers and light emitting diodes (LEDs) as transmitters. The deployment of LEDs in indoor VLC Systems is an important issue that affects the coverage of the network. In this article, we propose an improved version of Whale Optimization Algorithm, named EWOA, to resolve the LEDs placement problem in indoor visible light communication (VLC) systems. The EWOA is based on the integration of chaotic map concept and Opposition based learning method (OBL) into the standard WOA to improve its optimization performance. By taking into account the user throughput and coverage metrics while employing several produced instances and evaluating results against some meta-heuristics, the usefulness of EWOA was confirmed. The meta-heuristics that we used in the comparison are WOA, (MRFO) Manta Ray Foraging Optimizer, (CHIO) Herd immunity coronavirus optimizer, (MPA) Marine Predator Algorithm, (BA) Bat Algorithm, and (PSO) Particle Swarm Optimizer. The results showed that EWOA is more effective in finding optimal LEDs positions.
A hybrid whale optimization algorithm with tabu search algorithm for resource allocation in indoor VLC systems
(Elsevier, 2023) Yahia, Selma; Meraihi, Yassine; Mirjalili, Seyedali; Taleb, Sylia Mekhmoukh; Refas, Souad; Ramdane-Cherif, Amar; Eldeeb, Hossien B.
This paper proposes a novel hybrid approach (WOATS) based on the hybridization of Whale Optimization Algorithm (WOA) with Tabu search Algorithm (TS) for solving the resource allocation problem for indoor multi-user downlink VLC systems. The efficiency of the proposed WOATS is validated in several scenarios under different settings, considering the throughput and fairness parameters. The results demonstrated that WOATS provides competitive performance in optimizing resource allocation in indoor VLC systems compared to WOA, TS, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Moth Flame Optimization (MFO), Grey Wolf Optimizer (GWO), and Sine Cosine Algorithm (SCA).
Machine learning-based research for COVID-19 detection, diagnosis, and prediction : a survey
(Springer, 2022) Meraihi, Yassine; Gabis, Asma Benmessaoud; Mirjalili, Seyedali; Ramdane-Cherif, Amar; Alsaadi, Fawaz E
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,..) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed
Mesh router nodes placement for wireless mesh networks based on an enhanced Moth–Flame optimization algorithm
(Springer, 2023) Mekhmoukh Taleb, Sylia; Meraihi, Yassine; Mirjalili, Seyedali; Acheli, Dalila; Ramdane-Cherif, Amar; Benmessaoud Gabis, Asma
This paper proposes an enhanced version of Moth Flame Optimization (MFO) algorithm, called Enhanced Chaotic Lévy Opposition-based MFO (ECLO-MFO) for solving the mesh router nodes placement problem in wireless mesh network (WMN-MRNP). The proposed ECLO-MFO incorporates three strategies including the chaotic map concept, the Lévy flight strategy, and the Opposition-Based Learning (OBL) technique to enhance the optimization performance of MFO. Firstly, chaotic maps are used to increase the chaotic stochastic behavior of the MFO algorithm. Lévy flight distribution is adopted to increase the population diversity of MFO. Finally, OBL is introduced to improve the convergence speed of MFO and to explore the search space effectively. The effectiveness of the proposed ECLO-MFO is tested based on various scenarios under different settings, considering network connectivity and client coverage metrics. The results of simulation obtained using MATLAB 2020a demonstrate the accuracy and superiority of ECLO-MFO in determining the optimal positions of mesh routers when compared with the original MFO and ten other optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), Harmony Search (HS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), Bat Algorithm (BA), Firefly optimization (FA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA)
Nodes placement in wireless mesh networks using optimization approaches : a survey
(Springer, 2022) Mekhmoukh Taleb, Sylia; Meraihi, Yassine; Benmessaoud Gabis, Asma; Mirjalili, Seyedali; Ramdane-Cherif, Amar
Wireless mesh networks (WMNs) have grown substantially and instigated numerous deployments during the previous decade thanks to their simple implementation, easy network maintenance, and reliable service coverage. Despite these proprieties, the nodes placement of such networks presents many challenges for network operators. In this paper, we present a survey of optimization approaches implemented to address the WMNs nodes placement problem. These approaches are classified into four main categories: exact approaches, heuristic approaches, meta-heuristic approaches, and hybrid approaches. For each category, a critical analysis is drawn according to targeted objectives, considered constraints, type of positioned nodes (Mesh Router and Mesh Gateway), location (discrete or continuous), and environment (static or dynamic). In the end, several new key search areas for WMNs nodes placement are suggested
Strength prediction of a steel pipe having a hemi-ellipsoidal corrosion defect repaired by GFRP composite patch using artificial neural network
(Elsevier, 2023) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Le Thanh, Coung; Mirjalili, Seyedali; Magd, Abdel Wahab
Local stress concentration occurs when faults are present in pipelines under pressure. An example of such defects is the problem of corrosion caused by the environment in the field of pipeline installation. In the first part of this paper, we attempt to model the corrosion in the hemi-ellipsoidal form in order to study the locations of stress concentration in the specimens by several experimental cases and their influence on the stress resistance. The Gurson-Tvergaard-Needleman (GTN) mesoscopic damage model is used to simulate the specimens with good accuracy. In the second part, the investigation is extended to a pipe under static pressure with and without the presence of a glass fibre reinforced polymer (GFRP) composite patch. The maximum stress and percent stress reduction in a defected pipe with a hemi-ellipsoidal defect are determined using a 3D finite element model. This part examines the impact of the geometry of the composite patches on the percentage reduction of the maximum stresses in a section of pipeline subjected to static pressure. In the third part, the stresses and the percentage reduction in the maximum stresses are predicted using an artificial neural network (ANN). An inverse problem using ANN and Jaya algorithm is proposed to predict the group level of different sizes of defects under composite patches based on the maximum stress and percentage reduction of stress that the pipe withstands. The new method relates directly to real-world pipeline construction and repair applications. It could be also used for structural safety monitoring