Browsing by Author "Ait Saadi, Amylia"

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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
Coordination of scout drones (UAVs) in smart-city to serve autonomous vehicles
(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2023) Ait Saadi, Amylia; Meraihi, Yassine(Directeur de thèse); Ramdane-Cherif, Amar(Directeur de thèse)
The subject of Unmanned Aerial Vehicles (UAVs) has become a promising study field in both research and industry. Due to their autonomy and efficiency in flight, UAVs are considerably used in various applications for different tasks. Actually, the autonomy of the UAV is a challenging issue that can impact both its performance and safety during the mission. During the flight, the autonomous UAVs are required to investigate the area and determine efficiently their trajectory by preserving their resources (energy related to both altitude and path length) and satisfying some constraints (obstacles and axe rotations). This problem is defined as the UAV path planning problem that requires efficient algorithms to be solved, often Artificial Intelligence algorithms. In this thesis, we present two novel approaches for solving the UAV path planning problem. The first approach is an improved algorithm based on African Vultures Optimization Algorithm (AVOA), called CCO-AVOA algorithms, which integrates the Chaotic map, Cauchy mutation, and Elite Opposition-based learning strategies. These three strategies improve the performance of the original AVOA algorithm in terms of the diversity of solutions and the exploration/exploitation search balance. A second approach is a hybrid-based approach, called CAOSA, based on the hybridization of Chaotic Aquila Optimization with Simulated Annealing algorithms. The introduction of the haotic map enhances the diversity of the Aquila Optimization (AO), while the Simulated Annealing (SA) algorithm is applied as a local search algorithm to improve the exploitation search of the traditional AO algorithm. Finally, the autonomy and efficiency of the UAV are tackled in another important application, which is the UAV placement problem. The issue of the UAV placement relays on finding the optimal UAV placement that satisfies both the network coverage and connectivity while considering the UAV’s limitation from energy and load. In this context, we proposed an efficient hybrid called IMRFO-TS, based on the combination of Improved Manta Ray Foraging Optimization, which integrates a tangential control strategy and Tabu Search algorithms
A novel hybrid Chaotic Aquila Optimization algorithm with Simulated Annealing for Unmanned Aerial Vehicles path planning
(Elsevier, 2022) Ait Saadi, Amylia; Meraihi, Yassine; Soukane, Assia; Benmessaoud Gabis, Asma; Amar Ramdane, Cherif
In recent years, research on Unmanned Aerial Vehicles (UAVs) has become one of the interest- ing topics for industry and academic. UAV path plan- ning is one of the critical issues in terms of guaran- teeing the autonomy and good performance of UAVs in real-world applications. Its main objective is to de- termine and ensure an optimal and collision-free path between two positions from a starting point (source) to a destination one (target) while satisfying some UAV requirements (i.e. UAV’s safety, environment complex- ity, obstacle avoidance, energy consumption,etc). Due to the complexity of this topic, an efficient path plan- ning algorithm is required. This paper presents an opti- mal and hybrid algorithm, called CAOSA, based on the hybridization of Chaotic Aquila Optimization (CAO) and Simulated Annealing (SA) algorithms for solving the UAV path planning problem in a 3D environment. As a first step, chaotic map is introduced to enhance the chaotic stochastic behavior of the Aquila Optimization (AO) algorithm. In the second step, the SA algorithm is combined with CAO algorithm to improve the best so- lution (path quality) obtained after each iteration of COA. The main purpose of using SA is to increase the exploitation by searching for the most promising regions identified by the CAO algorithm. The perfor- mance of the proposed CAOSA algorithm is evaluated on several scenarios under different settings consider- ing the fitness value, path cost, and execution time metrics. Simulation results showed superiority and ro- bustness of CAOSA algorithm compared to nine meta- heuristics such as Simulated Annealing (SA), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Fire- fly optimization (FA), Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Al- gorithm (WOA), Dragonfly Algorithm (DA), and the original Aquila Optimization (AO). It is also revealed that CAOSA can offer an optimized path that improves UAV path planning requirements significantly in com- plex environments
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).