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Particle Swarm Optimization Algorithm(MATLAB Implementation)
Rating: 4.3 out of 5(181 ratings)
1,564 students

Particle Swarm Optimization Algorithm(MATLAB Implementation)

Solving Engineering Optimization Problems using Particle Swarm Optimization algorithm (MATLAB Implementation)
Created byMayank Dadge, Dr. H. T. Jadhav
Last updated 2/2022
English
English [Auto],

What you'll learn

  • Learn the basics of MATLAB programming
  • Understand Particle Swarm Optimization (PSO) algorithm
  • Implement PSO algorithm in MATLAB to solve benchmark functions
  • Implement PSO algorithm to solve a mechanical engineering optimization problem
  • Work on a research problem in the field of optimization

Course content

4 sections • 24 lectures • 2h 59m total length

  • Introduction2:15

    Explore MATLAB’s matrix laboratory for numerical computation and visualization, and learn the software interface—menu bar, editor, command window, and workspace that shows variables—designed for scientists and engineers.

  • Variables and Operators6:46

    Learn about variables and operators in MATLAB, where variables store values as matrices and names start with a letter, using arithmetic and relational operators for calculations and conditions.

  • Vector Declaration8:35

    Master MATLAB variable declarations by creating vectors and matrices with spaces, commas, semicolons, and the colon operator. Explore transpose, linspace, length, and the distinction between vectors and matrices.

  • indexing and Size of Vector5:06

    Master MATLAB vector indexing and sizing by learning 1D and 2D indexing, end and colon syntax, and the size and length functions with practical examples.

  • Matrix Operations7:20

    Master MATLAB matrix operations by applying scalars to each element. Understand matrix and element-wise addition, subtraction, multiplication, and division, including dot notation for element-wise operations and matrix multiplication rules.

  • max(), min(), ones() and zeros()4:22

    Explore MATLAB functions max and min for computing vector maxima and column-wise minimum values, and learn how to create matrices with ones and zeros.

  • rand(), randi() and repmat()3:19

    Explore MATLAB's rand, randi, and repmat functions to generate random numbers and matrices with specified sizes and ranges, including random integers and repeated patterns.

  • If Statement3:17

    Learn how to use the if statement in MATLAB to check conditions and act accordingly, illustrated by computing the positive difference between two numbers, including the if-then-else-end syntax.

  • For Loop5:45

    Master for loops in MATLAB to repeat tasks, iterate indices from 1 to 7, and apply nested loops to transform a 3x3 matrix from ones to zeros.

  • While Loop4:17

    Explore the while loop in matlab, compare it with the for loop, and learn its syntax, condition, and variable updates through counting 1 to 7 and summing to 325.

  • Functions6:59

    Learn to declare and use MATLAB functions to perform repeated operations on inputs, such as computing a vector’s average, with optional single-file or in-program definitions.

  • Plots3:55

    Master MATLAB plotting basics: use the block function to plot y = x^2 for x in 1..10, customize line styles, colors, and markers, and layer plots with hold on.

Requirements

  • Knowledge of high school mathematics is required
  • No programming knowledge in MATLAB is required. You'll learn everything you need in the course.
  • No prior knowledge of optimization is required

Description

This course is specifically developed for B. Tech. and M. Tech/MS students of all Engineering disciplines. Engineering students from all branches can take this course and apply the knowledge to solve optimization problems in their field as part of a mini-project or capstone project. In addition to this, the course is also useful to Ph. D. students of different engineering branches. This course can be taken by everyone irrespective of their programming knowledge. The basics of MATLAB programming is taught in the beginning of the course and the concept of optimization algorithms is also explained from the scratch. The content is so organized that the learner should be able to understand Engineering optimization from scratch and solve research problems leading to publication in an international journal of high repute. A guided project is also included at the end of the course to make sure students can apply the knowledge to real engineering optimization problems. It should be useful to students of all universities around the world.

This course is divided into FOUR Parts

  • Part I - Basics of MATLAB Programming

  • Part 2 - Concept of Optimization and Particle Swarm Optimization (PSO) algorithm

  • Part 3 - MATLAB Implementation of PSO algorithm to solve benchmark functions

  • Part 4 - MATLAB Implementation of PSO Algorithm to solve a typical Engineering optimization Problem 

Who this course is for:

  • Undergraduate, Post graduate Students and PhD scholars of all Engineering disciplines

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