Multi-Objective Optimization Using Genetic Algorithm: MATLAB
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 16 lectures (44m) | Size: 108.3 MB
A Quick Way to Learn and Solve Multi-Objective Optimization Problems in MATLAB. A Course for Beginners
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.
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
