Browsing by Author "Benayad, Abdelbaki"

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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.
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).
Use of artifcial intelligence (AI) methods for improving visible light communications in modern wireless networks
(Université M'Hamed Bougara Boumerdès : Faculté des Sciences, 2025) Benayad, Abdelbaki; Meraihi, Yassine(Directeur de thèse)
Visible Light Communication (VLC) has emerged as a promising wireless technology that utilizes Light Emitting Diodes (LEDs) for simultaneous illumination and high-speed data transmission. Compared to traditional Radio Frequency (RF) systems, VLC o?ers advantages such as higher data rates, enhanced security, and reduced electromagnetic interference. However, optimal LED placement remains a signi?cant challenge, as it directly impacts coverage, data throughput, and energy e?ciency. Given the NP-hard nature of this problem, conventional optimization methods are computationally infeasible for large-scale deployments. This research focuses on developing and enhancing meta-heuristic optimization algorithms to e?ciently address the LED placement challenge in VLC networks. We propose and evaluate three advanced techniques: Enhanced Whale Optimization Algorithm (EWOA), Hybrid Coronavirus Herd Immunity Optimize with Fire?y Algorithm (ICHIOFA), and a Multi-Objective Puma Optimizer (MOPO). These approaches integrate chaotic maps, Opposition-Based Learning (OBL), non-dominated sorting, and crowding distance mechanisms to improve search e?ciency, convergence speed, and solution quality. Extensive benchmarking against state-of-the-art meta-heuristics demonstrates that our proposed methods signi?cantly outperform existing algorithms in solution quality, robustness, and computational e?ciency. The ?ndings of this research contribute to advancing optimization techniques in VLC systems, providing scalable and e?cient solutions, to deployment in next-generation communication networks