Particle Swarm Optimization : a tutorial

Tharwat, A, Gaber, T ORCID:, Hassanien, AE and Elnaghi, BE 2017, 'Particle Swarm Optimization : a tutorial' , in: Handbook of Research on Machine Learning Innovations and Trends , Advances in Computational Intelligence and Robotics (ACIR), 2 , IGI Global, pp. 614-635.

PDF - Published Version
Download (1MB) | Preview
[img] PDF - Accepted Version
Restricted to Repository staff only

Download (1MB)
Access Information: Copyright © 2017 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. This final typeset PDF (which includes the title page, table of contents and other front materials, and the copyright statement) of the chapter has been deposited in line with IGI Global's Fair Use Policy:


Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

Item Type: Book Section
Editors: Gaber, T and Hassanien, AE
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Publisher: IGI Global
Series Name: Advances in Computational Intelligence and Robotics (ACIR)
ISBN: 9781522522294 (print); 9781522522300 (ebook)
ISSN: 2327-0411
Related URLs:
Depositing User: Dr. Tarek Gaber
Date Deposited: 24 Jun 2021 08:45
Last Modified: 16 Feb 2022 07:21

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year