Publications Scientifiques

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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
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    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.
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    Solving the LEDs placement problem in indoor VLC system using a hybrid coronavirus herd immunity optimizer
    (Springer Nature, 2024) Benayad, Abdelbaki; Boustil, Amel; Meraihi, Yassine; Yahia, Selma; Mekhmoukh Taleb, Sylia; Ait Saadi, Amylia; Ramdane-Cherif, Amar
    Visible light communication (VLC) is a developing technology enabling simultaneous illumination and communication between users. This is achieved by employing light emitting diodes (LEDs) as transmitters and photo-detectors (PDs) as receivers. In indoor visible light communication (VLC) systems, a significant challenge is the deployment of a various number of LEDs that accommodate different numbers of users. This particular problem falls under the category of Non-deterministic polynomial-time hard (NP-hard), making it difficult to find exact solutions in a reasonable amount of time. As a result, employing approximation approaches, particularly meta-heuristics, proves to be a suitable and effective way to address this challenge. In this paper, we propose a hybrid approach (ICHIO-FA) based on the combination of improved coronavirus herd immunity optimizer (ICHIO) with firefly algorithm (FA) for solving the LEDs placement problem in an indoor VLC system. In the proposed ICHIO-FA algorithm, the chaotic map concept is adopted to increase the chaotic stochastic behavior of the CHIO. Moreover, the opposition-based learning (OBL) mechanism is applied to enhance the convergence speed of CHIO and explore the search space effectively. Finally, FA is used as a local search method for ICHIO to avoid trapping into local optima. The effectiveness of the proposed ICHIO-FA algorithm is tested on several scenarios under different settings, taking into account the throughput and user coverage metrics. Simulation results demonstrate the accuracy and superiority of the ICHIO-FA approach in finding optimal LEDs positions when compared with the standard CHIO, FA, particle swarm optimization (PSO), genetic algorithm (GA), marine predators algorithm (MPA), whale optimization algorithm (WOA), manta ray foraging optimization (MRFO), bat algorithm (BA), grey wolf optimizer (GWO), and simulated annealing (SA).
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    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)
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    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
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    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
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    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
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    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)
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    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
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    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