Particle Swarm Optimization in MATLAB

Particle Swarm Optimization in MATLAB, available at Free, has an average rating of 4.41, with 11 lectures, based on 1893 reviews, and has 29534 subscribers.

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You will learn about Undertand what is Particle Swarm Optimization (PSO) and how it works Implement PSO in MATLAB from scratch Improve the PSO using Constriction Coefficients Solve optimization problems using PSO This course is ideal for individuals who are Students working on optimization problems and methods, specially engineering and science students, can use PSO as an optimization tool; so this course can help them to enhance their knowlodge about one of most useful meta-heuristics. or Anyone who is interested in artifical and computational intelligence will find this course useful. It is particularly useful for Students working on optimization problems and methods, specially engineering and science students, can use PSO as an optimization tool; so this course can help them to enhance their knowlodge about one of most useful meta-heuristics. or Anyone who is interested in artifical and computational intelligence will find this course useful.

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Summary

Title: Particle Swarm Optimization in MATLAB

Price: Free

Average Rating: 4.41

Number of Lectures: 11

Number of Published Lectures: 11

Number of Curriculum Items: 11

Number of Published Curriculum Objects: 11

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Undertand what is Particle Swarm Optimization (PSO) and how it works

Implement PSO in MATLAB from scratch

Improve the PSO using Constriction Coefficients

Solve optimization problems using PSO

Who Should Attend

Students working on optimization problems and methods, specially engineering and science students, can use PSO as an optimization tool; so this course can help them to enhance their knowlodge about one of most useful meta-heuristics.

Anyone who is interested in artifical and computational intelligence will find this course useful.

Target Audiences

Students working on optimization problems and methods, specially engineering and science students, can use PSO as an optimization tool; so this course can help them to enhance their knowlodge about one of most useful meta-heuristics.

Anyone who is interested in artifical and computational intelligence will find this course useful.

Particle Swarm Optimization (PSO) is an intelligent optimization algorithm based on the Swarm Intelligence. It is based on a simple mathematical model, developed by Kennedy and Eberhart in 1995, to describe the social behavior of birds and fish. The model relies mostly on the basic principles of self-organization which is used to describe the dynamics of complex systems. PSO utilizes a very simplified model of social behavior to solve the optimization problems, in a cooperative and intelligent framework. PSO is one of the most useful and famous metaheuristics and it is successfully applied to various optimization problems.

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. Next, PSO is implemented line-by-line and from scratch, and every line of code is described in detail. The instructor of this course is Dr. S. Mostapha Kalami Heris, Control and Systems Engineering PhD and member of Yarpiz Team.

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 MATLAB code and rewrite the PSO code in those languages.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Theoretical Foundations of PSO

Lecture 1: History of PSO and its Simplified Model

Lecture 2: Mathematical Model of PSO

Chapter 3: Implementation of PSO in MATLAB

Lecture 1: Optimization Problem Definition

Lecture 2: PSO Parameters

Lecture 3: Initialization of PSO

Lecture 4: PSO Main Loop

Lecture 5: Finalizing the Optimization Process

Chapter 4: Improving the Code

Lecture 1: Converting the Code to a Function

Lecture 2: Adding Position and Velocity Bounds

Lecture 3: Constriction Coefficients for PSO

Instructors

Yarpiz Team
Academic Education and Research Group

Mostapha Kalami Heris
Programmer and Instructor

Rating Distribution

1 stars: 11 votes

2 stars: 22 votes

3 stars: 161 votes

4 stars: 651 votes

5 stars: 1048 votes

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