A Comparison Study of PSO with Different Update Equations in Solving Economic Dispatch Problem

A number of particle swarm optimization (PSO) algorithms have been proposed for solving power economic dispatch (ED) problems. The crucial differences among these PSO are the updating equations they utilized. In this paper, we conduct a comparison study of four PSO with different updating equations,...

Full description

Saved in:
Bibliographic Details
Published inChinese Control Conference pp. 6028 - 6032
Main Authors Chen, Naiyuan, Zhou, Huiting
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2020
Subjects
Acceleration
Convergence
Economic dispatch
Economics
Fuels
Generators
Mathematical model
Particle swarm optimization
performance comparison
update equation
Online AccessGet full text
ISSN1934-1768
DOI10.23919/CCC50068.2020.9189202

Cover

More Information
Summary:A number of particle swarm optimization (PSO) algorithms have been proposed for solving power economic dispatch (ED) problems. The crucial differences among these PSO are the updating equations they utilized. In this paper, we conduct a comparison study of four PSO with different updating equations, i.e., inertia weight PSO (WPSO), bare-bone PSO (BBPSO), quantum-behaved PSO (QPSO) and biogeography-based learning PSO (BLPSO), for solving ED problems with various physical constraints. Based on the simulation results and analysis, some useful insights are concluded, which may provide some guidelines on the choice of PSO for solving the ED problems.
ISSN:1934-1768
DOI:10.23919/CCC50068.2020.9189202