An effective particle swarm optimization algorithm with disturbance

Xiangyu Ba

doi:10.1117/12.3031091

19 July 2024 An effective particle swarm optimization algorithm with disturbance

Xiangyu Ba

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318154 (2024) https://doi.org/10.1117/12.3031091
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Particle swarm optimization (PSO) has been widely studied because of its advantages of fast convergence and few control parameters, therefore many improvements have been proposed. However, some variants of PSO inevitably increase the parameters needed to tune and computation cost and lose the advantages of faster convergence and easier implementation of the PSO algorithm. To overcome these problems, this paper proposes an improved PSO, which is called EPSO. Compared with the standard particle swarm optimization algorithm, the acceleration coefficient is removed and the learning factor is replaced to reduce the algorithm’s dependency on its parameters. Noise-based disturbance is introduced to escape local optima. Afterward, a large number of benchmark functions were used to detect the performance of EPSO, and experimental results showed that it had higher accuracy and faster convergence speed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangyu Ba "An effective particle swarm optimization algorithm with disturbance", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318154 (19 July 2024); https://doi.org/10.1117/12.3031091

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Particle swarm optimization

Evolutionary algorithms

Particles

Statistical analysis

Evolutionary optimization

RELATED CONTENT

SDN routing strategy based on genetic algorithm and particle swarm...
Proceedings of SPIE (April 13 2023)

Differential adaptive SA-DEPSO algorithm based on particle swarm
Proceedings of SPIE (June 16 2023)

Chaotic multi-group optimization algorithm based on heterogeneous computing
Proceedings of SPIE (December 07 2023)

An improved PSO algorithm for optimizing land resource allocation
Proceedings of SPIE (October 16 2024)

Research on short term power load forecasting based on big...
Proceedings of SPIE (June 01 2023)

Solving TSP problem based on discrete snake optimization algorithm
Proceedings of SPIE (March 27 2024)

Multiobjective evolutionary optimization design of vehicle magnetorheological fluid damper
Proceedings of SPIE (November 01 2007)

Subscribe to Digital Library

Receive Erratum Email Alert

Keywords/Phrases

Search In:

Publication Years