Incremental Particle Swarm Optimization

Xu, Xin; Pan, Zhoujin; Xi, Yanqiu; Chen, Ling

doi:10.1016/j.phpro.2012.02.204

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar concept is seen in Particle Swarm Optimization where there is a population known as swarm which consists of a number of random solutions known as particles [15]. These particles are free to move around in the search-space based on certain formulae in a multi-dimensional space based on their previous movements and also considering the movements of their neighbors [29,27].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Bio Inspired Modified Internal Model Control Approach for Improved Disturbance Rejection of Piezo Micro Manipulator

Shome¹,

Jana²,

Mukherjee³

et al. 2018

STUD INFORM CONTROL

View full text Add to dashboard Cite

Classical Internal Model Control based approach for controller design has been used in different industrial control applications as it allows good set point tracking performance, especially for processes neglecting time delay. However, in many process control applications including nonlinear piezo electric actuation (PZA), disturbance rejection plays an important role compared to set point tracking. The present research firstly proposes an optimal filter design in series with a Modified Internal Model Control (M-IMC) based Proportional-Integral-Derivative (PID) controller for better set point tracking, improved disturbance rejection with reduced controller hardware resource requirement compared to classical IMC. Two efficient swarm intelligence based evolutionary soft computational techniques viz. Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO) are then exploited towards optimizing a control evaluation index based fitness function to design the M-IMC control parameters, including filter time constant. The distillation of bio inspired principles in control is seen to exhibit exciting results when the optimized parameters are utilized in the piezo plant modeled using a Dahl based second order system. The performance of the controller has been evaluated by subjecting the plant to several perturbations as well as to external disturbances. The results illustrate the efficiency of the PSO based M-IMC over other controllers.

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Bio Inspired Modified Internal Model Control Approach for Improved Disturbance Rejection of Piezo Micro Manipulator

Shome¹,

Jana²,

Mukherjee³

et al. 2018

STUD INFORM CONTROL

View full text Add to dashboard Cite

show abstract

Fuzzy Hierarchical Model and Particle Swarm Optimization in Gas Leakage Detector Mobile Robot

Miraswan

Buchari

Kurniati

2019

2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

View full text Add to dashboard Cite

Energy Efficiency in Modern Power Systems Utilizing Advanced Incremental Particle Swarm Optimization–Based OPF

2023

View full text Add to dashboard Cite

Since the power grid grows and the necessity for higher system efficiency is due to the increasing number of renewable energy penetrations, power system operators need a fast and efficient method of operating the power system. One of the main problems in a modern power system operation that needs to be resolved is optimal power flow (OPF). OPF is an efficient generator scheduling method to meet energy demands with the aim of minimizing the total production cost of power plants while maintaining system stability, security, and reliability. This paper proposes a new method to solve OPF by using incremental particle swarm optimization (IPSO). IPSO is a new algorithm of particle swarm optimization (PSO) that modifies the PSO structure by increasing the particle size, where each particle changes its position to determine its optimal position. The advantage of IPSO is that the population increases with each iteration so that the optimization process becomes faster. The results of the research on optimal power flow for energy generation costs, system voltage stability, and losses obtained by the IPSO method are superior to the conventional PSO method.

show abstract

Incremental Particle Swarm Optimization

Cited by 3 publications

References 5 publications

Bio Inspired Modified Internal Model Control Approach for Improved Disturbance Rejection of Piezo Micro Manipulator

Bio Inspired Modified Internal Model Control Approach for Improved Disturbance Rejection of Piezo Micro Manipulator

Fuzzy Hierarchical Model and Particle Swarm Optimization in Gas Leakage Detector Mobile Robot

Energy Efficiency in Modern Power Systems Utilizing Advanced Incremental Particle Swarm Optimization–Based OPF

Contact Info

Product

Resources

About