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    Advanced Trajectory Planning Technique for Unmanned Underwater Vehicle Navigation with Enhanced Fuzzy Logic Control and Obstacle Avoidance Strategy
    (Springer Science and Business Media, 2025) Demim, Fethi; Saghor, Sofian; Belaidi, Hadjira; Rouigueb, Abdenebi; Messaoui, Ali Zakaria; Benatia, Mohamed Akram; Chergui, Mohamed; Nemra, Abdelkrim; Allam, Ahmed; Kobzili, Elhaouari
    Trajectory planning plays a pivotal role in Unmanned Underwater Vehicles (UUVs), and this study addresses this aspect by employing Rapidly-exploring Random Trees (RRT) and a Fuzzy Logic Control (FLC). The investigation focuses on utilizing the RRT algorithm for waypoint generation in static environments. Leveraging Particle Swarm Optimization (PSO) enhances UUV control by optimizing FLC parameters, ensuring trajectory adherence to obstacle avoidance criteria. Through diverse experimental scenarios, the efficacy of the FLC regulator has been demonstrated, particularly in 3D waypoint navigation using Line-Of-Sight (LOS) guidance, showcasing accurate waypoint navigation, precise course maintenance, and effective pitch and yaw angle control for successful destination arrival. Moreover, this study highlights the increasing importance of RANS simulations in comprehending flow dynamics. It emphasizes a CFD-centric approach for design enhancement and aims to simulate 3D turbulent flow around UUV using ANSYS CFX code. This simulation evaluates appendage effects on overall drag and their interaction with the hull, effectively characterizing hydrodynamic behavior around the defined shape, aligning with study objectives.
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    Application mapping onto network on chip using particul swarm optimisation with genetic algorithm “GAPSO”
    (IEEE, 2022) Bougherara, Maamar; Amara, Rafik; Kemcha, Rebiha
    Network-on-chip is a new concept of interconnection in single-chip systems. This architecture is associated with the traditional methods of design of the interconnections which aims to carry out several functionalities and to stage the limits of that of the traditional methods. However, like any new technology, it requires research efforts, in particular to speed up and simplify the design phases. The mapping phase is a main step in the network-on-chip design process, it has a direct impact on system performance. This phase makes it possible to assign each task of an application to a physical resource while respecting the imposed constraints. This work aims to evaluate the performance for single-objective placement based on particle swarm optimization PSO and hybridization of this algorithm with the genetic algorithm