Parameter Settings in 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.

An Improved Particle Swarm Optimization Algorithm for Fractional Order PID Parameter Tuning

Revista de la Facultad de Ingeniería ◽

10.21311/002.31.7.04 ◽

2016 ◽

Keyword(s):

Fractional Order ◽

Optimization Algorithm ◽

Particle Swarm ◽

Parameter Tuning ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Fractional Order Pid

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

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v7i0.115 ◽

2016 ◽

Vol 7 ◽

pp. 1

Author(s):

Ravichander Janapati ◽

Ch. Balaswamy ◽

K. Soundararajan

Keyword(s):

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.

A hybrid population based incremental learning algorithm with Particle Swarm Optimization for general threshold Visual Cryptography schemes

Materials Today Proceedings ◽

10.1016/j.matpr.2020.12.1127 ◽

2021 ◽

Author(s):

Srinivasa Rao Kanusu ◽

Sridhar Mandapati

Keyword(s):

Incremental Learning ◽

Learning Algorithm ◽

Particle Swarm ◽

Visual Cryptography ◽

Population Based ◽

Hybrid Population ◽

Swarm Optimization

Research on torsional vibration modelling and control of printing cylinder based on particle swarm optimization

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/339/1/012031 ◽

2018 ◽

Vol 339 ◽

pp. 012031 ◽

Cited By ~ 1

Author(s):

Y M Wang ◽

W C Xu ◽

S Q Wu ◽

C W Chai ◽

X Liu ◽

...

Keyword(s):

Torsional Vibration ◽

Particle Swarm ◽

Swarm Optimization ◽

And Control

Simulation, design, hardware implementation, and control of a 9-level Flying Capacitor Multilevel Inverter with Particle Swarm Optimization algorithm

2012 IEEE 13th Workshop on Control and Modeling for Power Electronics (COMPEL) ◽

10.1109/compel.2012.6251745 ◽

2012 ◽

Cited By ~ 3

Author(s):

Ricardo Maldonado ◽

Carlos Gomez ◽

Marcel J. Castro Sitiriche

Keyword(s):

Optimization Algorithm ◽

Hardware Implementation ◽

Particle Swarm ◽

Multilevel Inverter ◽

Simulation Design ◽

Swarm Optimization ◽

And Control

Power System Stabilizers and Control of Reactive Power Compensator in Multi-Machine by Particle Swarm Optimization Algorithm

Indian Journal of Science and Technology ◽

10.17485/ijst/2015/v8i3/59595 ◽

2015 ◽

Vol 8 (3) ◽

pp. 254 ◽

Cited By ~ 4

Author(s):

H. Izadi ◽

R. Effatnejad

Keyword(s):

Power System ◽

Optimization Algorithm ◽

Reactive Power ◽

Particle Swarm ◽

Power System Stabilizers ◽

Swarm Optimization ◽

And Control ◽

Reactive Power Compensator

Proceedings of the 2015 International Conference on Electrical, Electronics and Mechatronics ◽

PID Controller Parameter Tuning Based on Improved Particle Swarm Optimization Algorithm

10.2991/iceem-15.2015.26 ◽

2015 ◽

Author(s):

Yanzi Miao ◽

Yang Liu ◽

Ying Chen ◽

Huijie Jin

Keyword(s):

Optimization Algorithm ◽

Pid Controller ◽

Particle Swarm ◽

Parameter Tuning ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Controller Parameter

Emerging Research on Swarm Intelligence and Algorithm Optimization - Advances in Computational Intelligence and Robotics ◽

Optimization of Drilling Process via Weightless Swarm Algorithm

10.4018/978-1-4666-6328-2.ch008 ◽

2015 ◽

pp. 181-190

Author(s):

T. O. Ting

Keyword(s):

Reduction Rate ◽

Particle Swarm ◽

Parameter Tuning ◽

Pso Algorithm ◽

Drilling Process ◽

Swarm Optimization ◽

Lower Standard ◽

Swarm Algorithm ◽

Novel Algorithm

In this chapter, the main objective of maximizing the Material Reduction Rate (MRR) in the drilling process is carried out. The model describing the drilling process is adopted from the authors' previous work. With the model in hand, a novel algorithm known as Weightless Swarm Algorithm is employed to solve the maximization of MRR due to some constraints. Results show that WSA can find solutions effectively. Constraints are handled effectively, and no violations occur; results obtained are feasible and valid. Results are then compared to previous results by Particle Swarm Optimization (PSO) algorithm. From this comparison, it is quite impossible to conclude which algorithm has a better performance. However, in general, WSA is more stable compared to PSO, from lower standard deviations in most of the cases tested. In addition, the simplicity of WSA offers abundant advantages as the presence of a sole parameter enables easy parameter tuning and thereby enables this algorithm to perform to its fullest.

Advances in Computational Intelligence and Robotics - Handbook of Research on Swarm Intelligence in Engineering ◽

Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models

10.4018/978-1-4666-8291-7.ch013 ◽

2015 ◽

pp. 406-434 ◽

Cited By ~ 3

Author(s):

Goran Klepac

Keyword(s):

Portfolio Management ◽

Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Optimal Values ◽

Input Variables ◽

Predictive Data Mining ◽

Customer Profiles

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive models as referent values for maximization value of probability that customers select/buy a product or service. Recognized results are used as a base for finding similar profiles between customers. The presented methodology has practical value for decision support in business, where information about customer profiles are valuable information for campaign planning and customer portfolio management.