Video Tutorial of Particle Swarm Optimization (PSO) in MATLAB

In this video tutorial, implementation of PSO in MATLAB is discussed in detail.
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Mise à jour 27 mai 2016

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In this video tutorial, implementation of Particle Swarm Optimization (PSO) in MATLAB is discussed in detail. In the first part, theoretical foundations of PSO is briefly reviewed. In the next two parts of this video tutorial, PSO is implemented line-by-line and from scratch, and every line of code is described in detail.
The video tutorial is available to watch online, via Yarpiz YouTube Channel, and to download as MP4 files. The project files of the video tutorial and lecture notes written by instructor during the course, are also available to download. The instructor of this course is Dr. S. Mostapha Kalami Heris, PhD of Control and Systems Engineering.
Video Files on YouTube

Part 1 of 3 - Theoretical Foundations:
https://youtu.be/sB1n9a9yxJk

Part 2 of 3 - Implementation in MATLAB:
https://youtu.be/xPkRL_Gt6PI

Part 3 of 3 - Improvements:
https://youtu.be/ICBYrKsFPqA

The course is also freely available via Udemy:
http://udemy.com/pso-in-matlab

For more information and download the video and project files and lecture notes for this tutorial, see: http://yarpiz.com/ytea101

After watching this video tutorial, you will be able to know what is PSO, and how it works, and how you can use it to solve your own optimization problems. Also, you will learn how to implement PSO in MATLAB programming language. If you are familiar with other programming languages, it is easy to translate the MATALB code and rewrite the PSO code in other programming languages.

Citation pour cette source

Yarpiz / Mostapha Heris (2024). Video Tutorial of Particle Swarm Optimization (PSO) in MATLAB (https://www.mathworks.com/matlabcentral/fileexchange/57286-video-tutorial-of-particle-swarm-optimization-pso-in-matlab), MATLAB Central File Exchange. Extrait(e) le .

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Créé avec R2012b
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YTEA101 PSO in MATLAB Video Tutorial/Files and Codes/

Version Publié le Notes de version
1.0.0.0

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