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Subject: search.filters.subject.Particle swarm optimization

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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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    New MPPT hybrid controller based on genetic algorithms and particle swarm optimization for photovoltaic systems
    (2023) Mammeri, Elhachemi; Ahriche, A.; Necaibia, A.; Bouraiou, A.
    Traditional Maximum Power Point Tracking (MPPT) techniques are unable to reach high performance in photovoltaic (PV) system under partial shading conditions because of the multi-peaks present in the Power-Voltage curve. For that, particle Swarm Optimization (PSO) and genetic algorithms (GA) have been combined in recent years. However, these algorithms demonstrate some drawbacks in tracking accuracy and convergence rates, which impair control performance. In this paper, a new controller based on hybridization of PSO and GA is introduced to track the global maximum power point (GMPP). The proposed algorithm (HPGA) increases the balance rate between exploration and exploitation due to the cascade design of GA and PSO. Thus, the GMPP tracking of both algorithms will be improved. Simulations are carried out based on ISOFOTON-75W PV modules to prove the high performance of the proposed algorithm. From the obtained results, we conclude that HPGA shows fast convergence and very good tracking accuracy of GMPP in PV system even under different shading patterns.
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    Big data clustering based on spark chaotic improved particle swarm optimization
    (Institute of Advanced Engineering and Science (IAES), 2024) Boushaki, Saida Ishak; Mahammed, Brahim Hadj; Bendjeghaba, Omar; Mosbah, Messaoud
    In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.
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    Improvement of system reliability in a natural gas processing facility by PSO and DE
    (Springer Nature, 2024) Saheb, Tafsouthe; Mellal, Mohamed Arezki
    The reliability of the systems as well as its optimization is the first concern of the designers. The elements of a given system can be either in series, parallel, parallel-series, or in a complex configuration. This paper addresses the reliability optimization of a natural gas processing facility. The reliability of this system is calculated and two redundancies strategies, active and standby, are optimized under the resource limits to improve reliability. Two bio-inspired optimization algorithms, namely the particle swarm optimization (PSO) and the differential evolution (DE), are implemented with penalty functions to find the optimal redundancy. The results obtained are compared.
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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
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    A predictive model of the optimal tool edge geometry for veneer cutting processes
    (Taylor et francis, 2019) Bouarab, Fatma Zohra; Aknouche, Abdelhamid H.; Hamrani, Abderrachid
    Veneer cutting is a specific machining process, where the chip is the final product. The objective of this article is to investigate on the optimal tool edge geometry, using particle swarm optimization (PSO) algorithm, to obtain the desired veneer thickness. The challenge is to maintain the best quality of veneer product with the control of pre-splitting condition and thickness variation. Numerical results obtained from PSO algorithm are compared and verified with the experimental ones. The proposed model allows us to predict the characteristic tool angles for different chip thicknesses and friction coefficients. For chip thickness range greater than 2 mm, the presplitting condition is no longer satisfied, as in the case of rotary peeling veneer, the need of using pressure bar becomes primordial.
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    Multi-objective factors optimization in fused deposition modelling with particle swarm optimization and differential evolution
    (Springer, 2022) Mellal, Mohamed Arezki; Laifaoui, Chahinaze; Ghezal, Fahima; Williams, Edward J.
    The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared
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    PID Control of DC Servo Motor using a Single Memory Neuron
    (IEEE, 2018) Ladjouzi, Samir; Grouni, Said; Soufi, Youcef
    In this paper, a novel approach to determine the optimal values of a PID controller is presented. The proposed method is based on using a single memory neuron which its weights represent the PID parameters. These weights are updated by the well-known bio-inspired algorithm: the particle swarm optimization. To show the efficiency of our method, we have applied it to control a DC servo motor which is used as an actuator for an arm robot manipulator. The obtained results are compared with those a fuzzy logic controller.
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    Crack size identification by means of model reduction using particle swarm optimization and genetic algorithm
    (2015) Benaissa, Brahim; Belaidi, Idir; Aït Hocine, Nourredine
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    Multimodal score-level fusion using hybrid ga-pso for multibiometric system
    (Slovene Society Informatika, 2015) Cherifi, Dalila; Hafnaoui, Imane; Nait Ali, Amine
    Due to the limitations that unimodal systems suffer from, Multibiometric systems have gained much interest in the research community on the grounds that they alleviate most of these limitations and are capable of producing better accuracies and performances. One of the important steps to reach this is the choice of the fusion techniques utilized. In this paper, a modeling step based on a hybrid algorithm, that includes Particle Swarm Optimization and Genetic Algorithm, is proposed to combine two biometric modalities at the score level. This optimization technique is employed to find the optimum weights associated to the modalities being fused. An analysis of the results is carried out on the basis of comparing the EER accuracies and ROC curves of the fusion techniques. Furthermore, the execution speed of the hybrid approach is discussed and compared to that of the single optimization algorithms, GA and PSO