Parameter identification of the phenomenological model for magnetorheological fluid dampers using hierarchic enhanced particle swarm optimization

2021 ◽  
Vol 35 (3) ◽  
pp. 875-887
Author(s):  
Jin Guo ◽  
Zhendong Li ◽  
Mengxuan Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Phenomenological Model ◽  
Particle Swarm ◽  
Magnetorheological Fluid ◽  
Swarm Optimization ◽  
Fluid Dampers ◽  
Enhanced Particle Swarm Optimization

scholarly journals Parameter Identification of Photovoltaic Cell Model Based on Enhanced Particle Swarm Optimization

2021 ◽  
Vol 13 (2) ◽  
pp. 840
Author(s):  
Rongjie Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Mean Squared Error ◽  
Cell Model ◽  
Particle Swarm ◽  
Photovoltaic Cell ◽  
Estimation Accuracy ◽  
Swarm Optimization ◽  
Minimum Mean Squared Error ◽  
Enhanced Particle Swarm Optimization

Photovoltaic (PV) cell (PVC) modeling predicts the behavior of PVCs in various real-world environmental settings and their resultant current–voltage and power–voltage characteristics. Focusing on PVC parameter identification, this study presents an enhanced particle swarm optimization (EPSO) algorithmto accurately and efficiently extract optimal PVC parameters. Specifically, the EPSO algorithm optimizes the minimum mean squared error between measured and estimated data and, on this basis, extractsthe parameters of the single-, double-, and triple-diode models and the PV module. To examine its effectiveness, the proposed EPSO algorithm is compared with other swarm optimization algorithms. The effectiveness of the proposed EPSO algorithm is validated through simulation. In addition, the proposed EPSO algorithm also exhibits advantages such as an excellent optimization performance, a high parameter estimation accuracy, and a low computational complexity.

scholarly journals Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

2020 ◽  
Vol 14 (1) ◽  
pp. 67-78
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Trial And Error Method ◽  
The Impact ◽  
Enhanced Particle Swarm Optimization ◽  
Error Method

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

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