Fuzzy and Position Particle Swarm Optimized Routing in VANET

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.

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End-to-End Delay Minimization in Multi-Channel, TDMA Wireless Sensor Networks by Particle Swarm Optimization

2018 International Conference on Advances in Computing and Communication Engineering (ICACCE) ◽

10.1109/icacce.2018.8441732 ◽

2018 ◽

Author(s):

Muhammad Anwar Hussain ◽

Myung J Lee

Keyword(s):

Wireless Sensor Networks ◽

Particle Swarm Optimization ◽

Sensor Networks ◽

Particle Swarm ◽

Wireless Sensor ◽

Swarm Optimization ◽

Delay Minimization ◽

End To End Delay ◽

End To End

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Application of Particle Swarm Optimization for Combined Environmental and Economic Dispatch of IEEE 30 Bus System Using Fuzzy Logic Technique

Advances in Intelligent and Soft Computing - Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012 ◽

10.1007/978-3-642-27443-5_81 ◽

2012 ◽

pp. 715-722

Author(s):

Sankaramurthy Padmini ◽

Teresa George ◽

Medepalli Sandeep

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Economic Dispatch ◽

Particle Swarm ◽

Swarm Optimization ◽

Fuzzy Logic Technique ◽

Bus System

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Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan ◽

10.32492/jintake.v9i1.720 ◽

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

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An Adaptive Particle Swarm Optimization based fuzzy logic controller for line of sight stabilization tracking and pointing application

2011 World Congress on Information and Communication Technologies ◽

10.1109/wict.2011.6141429 ◽

2011 ◽

Cited By ~ 3

Author(s):

Ravindra Singh ◽

M. Hanumandlu ◽

Shahida Khatoon ◽

Ibraheem

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Fuzzy Logic Controller ◽

Particle Swarm ◽

Line Of Sight ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization

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

Serbian Journal of Electrical Engineering ◽

10.2298/sjee0802247a ◽

2008 ◽

Vol 5 (2) ◽

pp. 247-262 ◽

Cited By ~ 17

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

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

International Journal of Computer Applications ◽

10.5120/12508-9121 ◽

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

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Prediction of TBM Penetration Rate Using Fuzzy Logic, Particle Swarm Optimization and Harmony Search Algorithm

Geotechnical and Geological Engineering ◽

10.1007/s10706-021-01982-x ◽

2021 ◽

Author(s):

Alireza Afradi ◽

Arash Ebrahimabadi ◽

Tahereh Hallajian

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Penetration Rate ◽

Search Algorithm ◽

Harmony Search ◽

Particle Swarm ◽

Harmony Search Algorithm ◽

Swarm Optimization ◽

Tbm Penetration Rate

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

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218816753 ◽

2019 ◽

Vol 41 (10) ◽

pp. 2886-2896 ◽

Cited By ~ 4

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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