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» » » Multi-objective Optimization Problems and Algorithms

Multi-objective Optimization Problems and Algorithms

Multi-objective Optimization Problems and Algorithms
BestSeller | h264, yuv420p, 1280x720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 4h 06 mn | 4.78 GB
Instructor: Prof. Seyedali Mirjalili
How to handle multiple objectives using a wide range of optimization algorithms
What you'll learn
Able to solve multi-objective problems
Able to use multi-objective optimization algorithms
Visualize the results of a multi-objective optimization
Analyze the results of a multi-objective optimization
Able to solve multi-objective optimization problems with a wide range of multi-objective techniques
Basic understanding of single-objective optimization
Familiar with Matlab programming language
Basic knowledge of Genetic Algorithms
Basic knowledge of Particle Swarm Optimization
This is an introductory course to multi-objective optimization using Artificial Intelligence search algorithms. We start with the details and mathematical models of problems with multiple objectives. Then, we focus on understanding the most fundamental concepts in the field of multi-objective optimization including but not limited to: search space, objective space, Pareto optimality, Pareto optimal solution set, Pareto optimal front, Pareto dominance, constraints, objective function, local fronts, local solutions, true Pareto optimal solutions, true Pareto optimal front, etc.
In the second part of this course, several optimization methods will be given to solve multi-objective optimization problems as follows:
No preference methods
A priori methods
A posteriori methods
Progressive methods
The course also includes a large number of coding videos to give you enough opportunity to practice the theory covered in the lecture. There are also several case studies including real-world problems that allow you to learn the process of solving challenging multi-objective optimization problems using multi-objective optimization algorithms.
For the search methods, we will be using stochastic optimization algorithms including Particle Swarm Optimization and Genetic Algorithms. This means that we develop Multi-Objective Particle Swarm Optimization (MOPSO) and multi-Objective Genetic Algorithms (MOGA).
Some of the reviews for this course are as follows:
Femi said: "As always, the instructor is expert in the course and explained in details with real-life examples, and I love his teaching style, even though the course is a bit tough, he made it fun!"
Pankaj said: "Dr Mirjalili teaches with a very good pace and conveys the concept clearly. The examples he uses are very relatable and he makes learning tricky concepts really fun."
Oyakhilome said: "Another great course by Dr. Seyedali. All components of the course were well structured and tailored to meet the educational needs of the students. I strongly recommend this course to everyone new to the field of optimization."
Join 1000+ students and start your optimization journey with us. If you are in any way not satisfied, for any reason, you can get a full refund from Udemy within 30 days. No questions asked. But I am confident you won't need to. I stand behind this course 100% and am committed to help you along the way.
Who this course is for:
Anyone who wants to solve multi-objective optimizations
Anyone who wants to use multi-objective algorithms
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