Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Multi-Objective Optimization Using Genetic Algorithm: MATLAB
Rating: 4.3 out of 5(44 ratings)
1,185 students

Multi-Objective Optimization Using Genetic Algorithm: MATLAB

A Quick Way to Learn and Solve Multi-Objective Optimization Problems in MATLAB. A Course for Beginners.
Created byKarthik Karunakaran, Ph.D.
Last updated 6/2022
English
English

What you'll learn

  • Basic concepts of Multi-Objective Optimization using Genetic Algorithm.
  • Understand the importance of optimization.
  • Formulate the optimization problem.
  • Implement the multi-objective optimization technique in MATLAB.

Course content

3 sections • 16 lectures • 44m total length

  • Introduction2:56

    discover multi-objective optimization in MATLAB through practical examples, using the global optimization toolbox, with code you can download, run, and modify while learning to optimize with several objectives.

  • Objective Function and Decision Variables1:21

    Learn how the objective function and decision variables, like X1 and X2, determine the objective in MATLAB, as you set up multi-objective optimization with a genetic algorithm.

  • Trade-Offs2:42

    Examine trade-offs between two objective functions F1 and F2 with a single variable X1 using MATLAB plots, and learn how multi objective optimization finds optimal tradeoffs under constraints and bounds.

  • Pareto Front1:56

    Explore multi objective optimization and the Pareto front, where trade-off solutions lie on a curve and Pareto optimal solutions are not outperformed by any other design points.

  • Fitness/Objective Functions, Individuals/genome and Score1:05

    Explore the terminology of objective functions by examining an input vector (2, -3, 1) as an individual and its corresponding score, illustrating how the objective value guides optimization.

  • Populations, Generations, Parents and Children1:44

    Demonstrates how a population represents individuals as a 100 x 3 matrix, iterating to form generations by selecting fit parents to produce children, guiding multi-objective optimization toward the Pareto front.

  • "gamultiobj" Algorithm1:40

    Use the MATLAB gamultiobj function to generate a Pareto front by evolving populations with crossover and mutation, selecting via ranking and crowding distance with elitism until stopping criteria are met.

Requirements

  • Elementary Mathematics.
  • MATLAB installed on the computer system.
  • No programming experience needed.

Description

Decision-makers in many areas, from industry to engineering and the social sector, face an increasing need to consider multiple, conflicting objectives in their decision processes. Such problems can arise in practically every field of science, engineering and business, and the demand for efficient and reliable solution methods is increasing. The task is challenging because, instead of a single optimal solution, multi-objective optimization results in many solutions with different trade-offs among criteria, also known as Pareto optimal solutions. A multi-objective Genetic Algorithm is a guided random search method suitable for solving problems with multiple objective functions and variables. Solutions of the Multi-objective Genetic Algorithm are illustrated using the Pareto fronts. Academics, industrial scientists, engineers engaged in research & development will find this course invaluable.

This course will teach you to implement multi-objective genetic algorithm-based optimization in the MATLAB environment using the Global Optimization Toolbox. Various kinds of optimization problems are solved in this course. At the end of this course, you will utilize the algorithm to solve your optimization problems. The complete MATLAB programs included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Take advantage of learning and understanding the fast-growing field of evolutionary computation.

Happy learning.

Who this course is for:

  • Students belonging to all disciplines of Engineering and Science
  • Students pursuing projects or research in Optimization
  • Working Professionals

Report abuse