scholarly journals OPTIMASI PENEMPATAN RECLOSER UNTUK MEMPERBAIKI KEANDALAN PADA PENYULANG LEMBONGAN MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION-FUZZY

2018 ◽  
Vol 5 (2) ◽  
pp. 129
Author(s):  
Jaime Luis da Costa ◽  
Rukmi Sari Hartati ◽  
Widyadi Setiawan
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Reliability Index ◽  
Particle Swarm ◽  
Network System ◽  
Combined Method ◽  
Swarm Optimization ◽  
The World ◽  
World Class ◽  
Group 5

In Bali, especially at Tiga Nusa, the need for electricity is increasing along with the development of the construction of resorts and hotels. In this study, a sample of cases was taken in 2014 regarding the Lembongan feeder network system which was still installed with several LBS (Load Break Switch) which used to localize interference. Therefore, to anticipate interference and improve the reliability of feeders, one of many ways is to add an automatic back cover (recloser). This study aims to determine the optimal recloser location in the Lembongan feeder by using a combined method of fuzzy logic and particle swarm optimization with the help of MATLAB software. The results of this study obtained the best location of the recloser in group 5, namely the area between LBS SD 3, LBS Celagi Empak, and LBS Lembongan by adding one recloser. The SAIFI value is 1,9471 times / customer / year and the SAIDI value is 1,4596 hours / customer / year where the world class service parameter standard for the SAIFI reliability index is 3 times / customer / year and the SAIDI reliability index is 2,5 hours / customer / year.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

scholarly journals Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

2008 ◽  
Vol 5 (2) ◽  
pp. 247-262 ◽  
Author(s):  
Boumediene Allaoua ◽  
Abderrahmani Abdessalam ◽  
Gasbaoui Brahim ◽  
Nasri Abdelfatah
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
Swarm Optimization

scholarly journals Fuzzy and Position Particle Swarm Optimized Routing in VANET

2021 ◽  
Vol 12 (4) ◽  
pp. 199-206
Author(s):  
Viswanathan Ramasamy ◽  
Jagatheswari Srirangan ◽  
Praveen Ramalingam
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Routing Algorithms ◽  
Transport Systems ◽  
Swarm Optimization ◽  
Packet Delivery ◽  
End To End Delay ◽  
End To End ◽  
Fuzzy Logic Technique

In Intelligent Transport Systems, traffic management and providing stable routing paths between vehicles using vehicular ad hoc networks (VANET's) is critical. Lots of research and several routing techniques providing a long path lifetime have been presented to resolve this issue. However, the routing algorithms suffer excessive overhead or collisions when solving complex optimization problems. In order to improve the routing efficiency and performance in the existing schemes, a Position Particle Swarm Optimization based on Fuzzy Logic (PPSO-FL) method is presented for VANET that provides a high-quality path for communication between nodes. The PPSO-FL has two main steps. The first step is selecting candidate nodes through collectively coordinated metrics using the fuzzy logic technique, improving packet delivery fraction, and minimizing end-to-end delay. The second step is the construction of an optimized routing model. The optimized routing model establishes an optimal route through the candidate nodes using position-based particle swarm optimization. The proposed work is simulated using an NS2 simulator. Simulation results demonstrate that the method outperforms the standard routing algorithms in packet delivery fraction, end-to-end delay and execution time for routing in VANET scenarios.

scholarly journals A New Optimal Fuzzy Logic Guidance Law using Time Variant Particle Swarm Optimization

2013 ◽  
Vol 72 (7) ◽  
pp. 34-37
Author(s):  
Labeed Hassan ◽  
Seyed Hossein Sadati ◽  
Mohamad Bagher Malaeak ◽  
Mohamad Ali Ashtiani ◽  
Jalal Karimi
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Guidance Law

Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms

2019 ◽  
Vol 41 (10) ◽  
pp. 2886-2896 ◽  
Author(s):  
Yang Chen ◽  
Dazhi Wang
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
General Type ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Fuzzy Logic Systems ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Logic Systems

Much more attention has been focused on studying and applying general type-2 fuzzy logic systems (GT2 FLSs) in recent years. The paper designs a type of Mamdani GT2 FLS for studying forecasting problems based on the data of permanent magnetic drive (PMD) loss. During the system design process, we choose the primary membership functions (MFs) of antecedent, consequent and input measurement general type-2 fuzzy sets (GT2 FSs) as Gaussian type MFs with uncertain standard deviations. The corresponding vertical slices (secondary MFs) are chosen as the triangle MFs. All the parameters of Mamdani GT2 FLSs are optimized by the quantum particle swarm optimization (QPSO) algorithms. Noisy data of PMD loss are adopted for both training and testing the proposed FLSs forecasting approaches. Simulation studies and convergence analysis are employed to show the effectiveness and feasibility of the proposed GT2 FLSs forecasting methods compared with their T1 and IT2 counterparts.

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