Multi-objective Optimization Problems and 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
Requirements
- Basic understanding of single-objective optimization
- Familiar with Matlab programming language
- Basic knowledge of Genetic Algorithms
- Basic knowledge of Particle Swarm Optimization
Description
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
Instructor
- 4.5 Instructor Rating
- 4,484 Reviews
- 17,792 Students
- 8 Courses
Professor Seyedali (Ali) Mirjalili is internationally recognized for his advances in Artificial Intelligence (AI) and optimization, including the first set of SI techniques from a synthetic intelligence standpoint - a radical departure from how natural systems are typically understood - and a systematic design framework to reliably benchmark, evaluate, and propose computationally cheap robust optimization algorithms. Prof. Mirjalili has published over 150 journal articles, many in high-impact journals, with one paper having over 4000 citations - the most cited paper in the Elsevier Advances in Engineering Software journal. In addition, he has more than five books, 30 book chapters, and 15 conference papers.
Prof. Mirjalili has over 50,000 citations in total with an H-index of 80. From Google Scholar metrics, he is globally one of the most-cited researchers in Artificial Intelligence. As the most cited researcher in Robust Optimization, he is in the list of 1% highly-cited researchers and named as one of the most influential researchers in AI by the world by Web of Science.
Ali is a senior member of IEEE and an associate editor of several journals including IEEE Access, Applied Soft Computing, Advances in Engineering Software, and Applied Intelligence. His research interests include Robust Optimization, Engineering Optimization, Multi-objective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is working on the application of multi-objective and robust meta-heuristic optimization techniques as well.
In addition to his excellent research outputs, Prof. Ali has been a teacher for over 15 years and a Udemy instructor for more than three years. He has 10,000+ students, and the majority of his courses have been highly ranked by both Udemy and students. He is the only Udemy instructor in the list of top 1% highly-cited researchers.