Small Population Based Modified Parallel Particle Swarm Optimization for Motion Estimation

2008 ◽  
Author(s):  
K. M. Bakwad ◽  
S.S. Pattnaik ◽  
B. S. Sohi ◽  
Swapna Devi ◽  
Sastry V. R. S. Gollapudi ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Estimation ◽  
Particle Swarm ◽  
Population Based ◽  
Small Population ◽  
Swarm Optimization ◽  
Parallel Particle Swarm Optimization

On-Line Parameters Identification for Excitation System Based on Small Population-Based Particle Swarm Optimization

2013 ◽  
Vol 291-294 ◽  
pp. 2159-2163
Author(s):  
Li Fang Lu ◽  
Huan Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Computation Time ◽  
Population Based ◽  
Small Population ◽  
Parameters Identification ◽  
System Stability ◽  
Excitation System ◽  
Swarm Optimization ◽  
On Line

Parameters identification of excitation system plays a key role for the power system stability analysis. In this paper, a small population-based particle swarm optimization (SPPSO) approach is used to acquire excitation system on-line model quickly and accurately. In the proposed approaches, three operations are introduced to improve the performance of the algorithm, namely mutation operation, DE-acceleration operation and migration operation. Furthermore, the BPA-FV practical model and the PMU data are adopted. The simulation results of the model obtained by SPPSO have been compared with that of the model obtained by other approach in literature and our reformulations. The SPPSO algorithm shows better performance on the convergence as well as computation time and effort.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

Parameter Settings in Particle Swarm Optimization

2017 ◽  
pp. 101-132
Author(s):  
Snehal Mohan Kamalapur ◽  
Varsha Patil
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Poor Performance ◽  
Parameter Tuning ◽  
Population Based ◽  
Parameter Control ◽  
Swarm Optimization ◽  
Study Parameter ◽  
Optimal Values ◽  
And Control

The issue of parameter setting of an algorithm is one of the most promising areas of research. Particle Swarm Optimization (PSO) is population based method. The performance of PSO is sensitive to the parameter settings. In the literature of evolutionary computation there are two types of parameter settings - parameter tuning and parameter control. Static parameter tuning may lead to poor performance as optimal values of parameters may be different at different stages of run. This leads to parameter control. This chapter has two-fold objectives to provide a comprehensive discussion on parameter settings and on parameter settings of PSO. The objectives are to study parameter tuning and control, to get the insight of PSO and impact of parameters settings for particles of PSO.

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