Multi-Objective Optimization Using Genetic Algorithm: MATLAB

Multi-Objective Optimization Using Genetic Algorithm: MATLAB, available at $54.99, has an average rating of 4.2, with 16 lectures, 1 quizzes, based on 37 reviews, and has 1154 subscribers.

You will learn about 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. This course is ideal for individuals who are Students belonging to all disciplines of Engineering and Science or Students pursuing projects or research in Optimization or Working Professionals It is particularly useful for Students belonging to all disciplines of Engineering and Science or Students pursuing projects or research in Optimization or Working Professionals.

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Summary

Title: Multi-Objective Optimization Using Genetic Algorithm: MATLAB

Price: $54.99

Average Rating: 4.2

Number of Lectures: 16

Number of Quizzes: 1

Number of Published Lectures: 16

Number of Published Quizzes: 1

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Fundamentals

Lecture 1: Introduction

Lecture 2: Objective Function and Decision Variables

Lecture 3: Trade-Offs

Lecture 4: Pareto Front

Lecture 5: Fitness/Objective Functions, Individuals/genome and Score

Lecture 6: Populations, Generations, Parents and Children

Lecture 7: "gamultiobj" Algorithm

Chapter 2: Script Objective Functions

Lecture 1: MATLAB Script For Objective Functions

Lecture 2: MATLAB Script For Vectorized Function Call

Chapter 3: Optimization Programming

Lecture 1: Simple Multiobjective Problem

Lecture 2: Multiobjective Problem with Two Objectives and Two Variables

Lecture 3: Multiobjective Problem with Linear Constraint

Lecture 4: Multiobjective Problem with Bounds

Lecture 5: Multiobjective Problem with Non-linear constraints

Lecture 6: Vectorized Fitness Function

Lecture 7: Maximizing Objective Functions

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 5 votes
4 stars: 14 votes
5 stars: 16 votes

Frequently Asked Questions

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