Browsing by Author "Ramdane-Cherif, Amar"

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A Comprehensive Survey of Manta Ray Foraging Optimization: Theory, Variants, Hybridization, and Applications
(Springer Science and Business Media, 2025) Yahia, Selma; Taleb, Sylia Makhmoukh; Ait Saadi, Amylia; Meraihi, Yassine; Bhuyan, Bikram Pratim; Mirjalili, Seyedali; Ramdane-Cherif, Amar
The Manta Ray Foraging Optimization (MRFO) algorithm is a recent Swarm-based meta-heuristic optimization algorithm inspired by the foraging behavior of manta rays in catching and hunting their prey, utilizing three main techniques (i.e.: chain foraging, somersault foraging, and cyclone foraging). Since its development by Zhao et al. (Neural Comput Appl 32:9777–9808, 2020; Eng Appl Artif Intell 87:103300, 2020), the MRFO algorithm has garnered significant attention among researchers and has been applied across various fields to solve real-world optimization problems. This is due to its simple structure, flexibility, ease of implementation, and reasonable convergence rate. This paper provides an extensive and in-depth survey of the MRFO algorithm including modification, multi-objective, and hybridized versions. It also examines the various applications of the MRFO algorithm in several domains of problems such as classification, feature selection, scheduling, robotics, photovoltaic power systems, optimal parameter control, and clustering. Furthermore, the results of the MRFO algorithm are compared with some well-regarded optimization meta-heuristics such as Differential Evolution (DE), Harmony Search (HS), Bat Algorithm (BA), Multi-Verse Optimizer (MVO), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Moth Flame Optimization (MFO), Henry Gas Solubility Optimization (HGSO), and White Shark Optimizer (WSO). Finally, the paper proposes some potential future research directions to further advance the MRFO’s capability and applicability
Advances in coyote optimization algorithm : variants and applications
(Springer, 2023) Meraihi, Yassine; Gabis, Asma Benmessaoud; Ramdane-Cherif, Amar; Acheli, Dalila
Coyote Optimization Algorithm (COA) is a recent population-based technique inspired by the attitude of coyotes in nature. COA has been widely applied to tackle different optimization issues in several areas and has proved its successfulness compared to several methods found in the literature. In this paper, we describe a brief overview of COA and its variants including adjusted and hybridized versions. Additionally, we present COA applications in various fields such as image segmentation, wireless mesh networks, economic dispatch, electric power systems, distributed generation, and other engineering issues. Finally, we recommend some interesting future research areas directions for COA
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.
Dragonfly algorithm: a comprehensive review and applications
(Springer, 2020) Meraihi, Yassine; Ramdane-Cherif, Amar; Acheli, Dalila; Mahseur, Mohammed
Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic andstatic swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared toseveral well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and itsnew variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in severalfields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the papersuggests some possible interesting research on the applications and hybridizations of DA for future works
Energy harvesting based on SLIPT in V2V-VLC system under atmospheric weather conditions
(2022) Refas, Souad Ikram; Acheli, Dalila; Yahia, Selma; Meraihi, Yassine; Ramdane-Cherif, Amar; Benmessaoud Gabis, Asma
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
Enhanced Whale Optimization Algorithm for mesh routers placement problem in wireless mesh networks
(IEEE, 2022) Mekhmoukh Taleb, Sylia; Meraihi, Yassine; Yahia, Selma; Benmessaoud Gabis, Asma; Ramdane-Cherif, Amar; Acheli, Dalila
In order to address the placement issue for mesh routers in wireless mesh networks, this study suggests an enhanced variant of the Whale optimization Algorithm (WOA), called ELG-WOA. The Generalized Opposition Based-Learning (GOBL) and the Levy Flight Distribution (LFD) are two methods that were incorporated into the original WOA to form the foundation of the ELG-WOA. Four performance indicators, including coverage, connectivity, load balancing, and fitness value, are used to validate the performance of ELG-WOA. The simulation results showed that ELG-WOA outperformed WOA and Bat Algorithm (BA)
An Enhanced white shark optimization algorithm for unmanned aerial vehicles placement
(Springer Nature, 2024) Saadi, Amylia Ait; Soukane, Assia; Meraihi, Yassine; Gabis, Asma Benmessaoud; Ramdane-Cherif, Amar; Yahia, Selma
In this chapter, we propose an Elite Opposition-Based White Shark Optimization (ELWSO) Algorithm, for tackling the Unmanned Aerial Vehicles (UAVs) Placement problem in smart cities. The proposed EWSO scheme is based on the incorporation of the Elite opposition-based strategy to ameliorate the optimization efficiency of the original WSO. EWSO was assessed in terms of fitness, coverage, and connectivity metrics under 23 cases with different numbers of UAVs and users. The results of simulated experiments, conducted using MATLAB 2021b version, revealed that the EWSO algorithm outperforms the basic WSO, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bat Algorithm (BA).
Hybrid whale optimization algorithm with simulated annealing for the UAV placement problem
(Springer Nature, 2024) Taleb, Sylia Mekhmoukh; Meraihi, Yassine; Yahia, Selma; Ramdane-Cherif, Amar; Gabis, Asma Benmessaoud; Acheli, Dalila
This chapter suggests a hybrid algorithm based on the combination of whale optimization algorithm (WOA) with simulated annealing (SA), called WOA-SA, for solving the unmanned aerial vehicle (UAV) placement problem. WOA-SA combines WOA’s global search functionality with SA’s local search functionality. The main objective of our work is to determine the optimal position of the UAV in order to maximize the total throughput, depending on a given set of user locations and traffic demands. The WOA-SA algorithm is validated in terms of the total throughput using 18 distinct instances with various numbers of users, taking into account the effect of the distribution of user positions. The results of simulation using Matlab demonstrated that the WOA-SA algorithm obtains better results than WOA, SA, Particle Swam Optimization (PSO), Genetic Algorithm (GA), and Bat Algorithm (BA).
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)
A Monadic Second-Order Temporal Logic framework for hypergraphs
(Springer Nature, 2024) Bhuyan, Bikram Pratim; Singh, Thipendra P.; Tomar, Ravi; Meraihi, Yassine; Ramdane-Cherif, Amar
This study introduces a novel computational framework integrating monadic second-order temporal logic (MSOTL) with hypergraph models to enhance the predictive analysis and prediction of complex systems, with a specific focus on urban agriculture. Traditional graph-based models often fail to capture the intricate, high-order temporal dynamics inherent in such systems. By leveraging the expressive power of MSOTL within a hypergraph context, our approach enables a more nuanced representation of temporal and relational data, leading to improved predictive accuracy and deeper analytical insights. The framework was applied to a comprehensive dataset of urban agricultural practices, incorporating data from diverse farming sites across multiple countries. Our results demonstrate the model’s capability to outperform existing methods in predicting agricultural outcomes by effectively capturing both the spatial and temporal complexities of urban farming data. The study not only advances the theoretical understanding of hypergraph-based temporal logic modeling but also offers an application for urban agricultural planning and management.
Multi-Directional Vehicle-To-Vehicle Visible Light Communication With Angular Diversity Technology
(IEEE, 2020) Yahia, Selma; Meraihi, Yassine; Benmessaoud Gabis, Asma; Ramdane-Cherif, Amar
In this paper, we investigate the performance of the multi-directional vehicle-to-vehicle (V2V) visible light communication (VLC) system by deploying the angle diversity technology. We consider a road with a multi-lanes configuration where multiple cars utilize their headlights to transmit the signals to a receiving car. Such car uses an angle diversity receiver consisting of three photodetectors (PDs) and oriented in different directions. The effect of the field-of-view angle (FoV) of the PD same as the semi-half angle of the transmitter on the received signal-to-noise-ratio (SNR) are investigated. We also investigate the impact of higher data rates on the system performance. The results show that the system can achieve an SNR higher than 13.6 dB at a transmission distance of 50 m. Such SNR value is required to achieve a reliable communication system with bit error rate (BER) less than 10 −3 .
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
On the performance of MIMO vehicular visible light communications
(Springer, 2023) Yahia, Selma; Meraihi, Yassine; Gabis, Asma Benmessaoud; Ramdane-Cherif, Amar
Vehicular communication has attracted much interest as one of the essential elements of intelligent transportation systems. It allows connectivity and data sharing between the traveling vehicles along the road in a way to improve road safety and offer comfortable driving during trips. In this paper, we investigate the performance of Multiple-Input Multiple-Output (MIMO) vehicular communication system utilizing the visible light communication (VLC) technology. Specifically, we consider a VLC based-vehicle-to-vehicle (V2V) system, where the two vehicles follow each other on a single-lane road. We construct a 2 × 2 MIMO transmission system utilizing the two headlights of the source vehicle as wireless transmitters while two photodetectors (PDs) are installed at the destination vehicle acting as wireless receivers. For obtaining the optical channel gain, we adopt the ray-tracing of OpticStudio taking into account the possibility of both vertical and horizontal displacements between the vehicles same as the effect of weather conditions. The received optical power and the bit-error-rate (BER) are investigated for each MIMO link between transmitters and receivers. The impact of displacements, weather conditions, and receiver apertures is also addressed
Performance analysis of bidirectional multi-hop vehicle-to-vehicle visible light communication
(Institute of Electrical and Electronics Engineers Inc, 2023) Refas, Souad; Acheli, Dalila; Yahia, Selma; Meraihi, Yassine; Ramdane-Cherif, Amar; Van, Nhan Vo; Ho, Tu Dac
Vehicular visible light communication (VVLC) has emerged as a promising field of research, garnering considerable attention from scientists and researchers. VVLC offers a potential solution to enable connectivity and communication between travelling vehicles along the road by using their existing headlights (HLs) and taillights (TLs) as wireless transmitters and integrating photodetectors (PDs) within the car front or car-back as wireless receivers. However, VVLC encounters more challenges than indoor VLC, particularly in vehicle-to-vehicle (V2V) communication, where vehicle mobility disrupts the establishment of direct communication links. To address this, we propose a multi-hop relay system wherein intermediate vehicles act as wireless relays to maintain a line-of-sight (LoS) link. In this paper, we investigate the performance of a bidirectional multi-hop relay V2V-VLC system that operates in both the forward and backward directions. Based on realistic ray tracing channel models, we derive a closed-form expression for the full bidirectional communication range. We also analyze how the transceiver's parameters and the number of relays affect the system performance. Our results show that the proposed bidirectional multi-hop relay system can extend the direct transmission range by more than 19 m with only a hop relay.
Performance evaluation of vehicular visible light communication based on angle-oriented receiver
(Elsevier, 2022) Yahia, Selma; Meraihi, Yassine; Ramdane-Cherif, Amar; Gabis, Asma Benmessaoud; Eldeeb, Hossien B.
Visible Light Communication (VLC) has emerged as a promising technology to complement radio frequency-based vehicular communications. Initial studies in Vehicle-to-Vehicle (V2V) VLC systems assumed that two vehicles follow each other with perfect alignment. Such idealistic assumption is not always maintained during traveling along the road. The lateral shift between the vehicles might strongly impact the system performance. In addition, the effect of the transceiver and system parameters on the performance of V2V-VLC systems should be taken into account. In this paper, we fill this research gap by investigating the performance of V2V-VLC systems under the impact of the lateral shift between the vehicles and transceiver parameters. Then, we introduce the use of the angle-oriented receiver (AOR) in V2V-VLC systems to enhance the system performance in terms of achievable capacity, maximum achievable distance, and packet delivery ratio (PDR). The AOR consists of multiple receiving elements oriented in different directions. We further investigate the impact of the number of AOR elements, both the field-of-view (FoV) and the aperture diameter of each receiving element, and the bandwidth on the system performance. Our results demonstrate that with a carefully chosen system and AOR parameters, a higher system capacity of up to 61 Mb/s is achieved at a communication distance of 50 m
Performance study and analysis of MIMO visible light communication-based V2V systems
(Springer, 2022) Yahia, Selma; Meraihi, Yassine; Refas, Souad; Benmessaoud Gabis, Asma; Ramdane-Cherif, Amar; Eldeeb, Hossien B.
Vehicular Visible Light Communication (VLC) has recently attracted much interest from researchers and scientists. This technology enables the connectivity between the vehicles and the infrastructures along the road utilizing the Lighting-Emitting-Diodes based vehicle HeadLights (HLs) and TailLights (TLs) as wireless transmitters. This paper investigates the performance of a Vehicle-to-Vehicle VLC system using a Multiple-Input Multiple-Output (MIMO) scheme. Specifically, we establish the MIMO transmission system by using the two HLs of the source vehicle as wireless transmitters and multiple receivers (RXs) installed at the rear of the destination vehicle as wireless receivers. We consider different numbers of RXs, which result in various MIMO configurations, i.e., 2 × 2 , 2 × 3 , and 2 × 4. We conduct a channel modeling study based on the non-sequential ray-tracing capabilities of the OpticStudio software to obtain the optical channel gain, considering the possibility of both horizontal and vertical displacement between vehicles. We then explore the contribution of each RX in the total received power. In addition, we investigate the effect of weather conditions, modulation orders, and artificial light sources on the bit error rate (BER) performance of the considered MIMO configurations. The obtained results demonstrate that deploying the MIMO with higher orders can significantly enhance the system performance, particularly when there is a lateral shift between the two cars. It has been drawn from our results that the required SNR to achieve a BER of 10- 4 reduces by 6 dB when 2 × 4 MIMO configuration is deployed compared to the 2 × 2 MIMO configuration