Optimization problems and algorithms

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  • Study type: Online
  • Starts: Anytime
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Course Description

This is an introductory course to the stochastic optimization problems and algorithms as the basics sub-fields in Artificial Intelligence. We will cover the most fundamental concepts in the field of optimization including metaheuristics and swarm intelligence. By the end of this course, you will be able to identify and implement the main components of an optimization problem. Optimization problems are different, yet there have mostly similar challenges and difficulties such as constraints, multiple objectives, discrete variables, and noises. This course will show you how to tackle each of these difficulties. Most of the lectures come with coding videos. In such videos, the step-by-step process of implementing the optimization algorithms or problems are presented. We have also a number of quizzes and exercises to practice the theoretical knowledge covered in the lectures.

Here is the list of topics covered:

  • History of optimization

  • Optimization problems

  • Single-objective optimization algorithms

  • Particle Swarm Optimization

  • Optimization of problems with constraints

  • Optimization of problems with binary and/or discrete variables

  • Optimization of problems with multiple objectives

  • Optimization of problems with uncertainties

Particle Swarm Optimization will be the main algorithm, which is a search method that can be easily applied to different applications including Machine Learning, Data Science, Neural Networks, and Deep Learning.

I am proud of 200+ 5-star reviews. Some of the reviews are as follows:

David said: "This course is one of the best online course I have ever taken. The instructor did an excellent job to very carefully prepare the contents, slides, videos, and explains the complicated code in a very careful way. Hope the instructor can develop much more courses to enrich the society. Thanks!"

Khaled said: "Dr. Seyedali is one of the greatest instructor that i had the privilege to take a course with. The course was direct to the point and the lessons are easy to understand and comprehensive. He is very helpful during and out of the course. i truly recommend this course to all who would like to learn optimization\PSO or those who would like to sharpen their understanding in optimization. best of luck to all and THANK YOU Dr. Seyedali."

Biswajit said: "This coursework has really been very helpful for me as I have to frequently deal with optimization. The most prominent feature of the course is the emphasis given on coding and visualization of results. Further, the support provided by Dr. Seyedali through personal interaction is top notch.


Boumaza said: "Good Course from Dr. Seyedali Mirjalili. It gives us clear picture of the algorithms used in optimization. It covers technical as well as practical aspects of optimization. Step by step and very practical approach to optimization through well though and properly explained topics, highly recommended course You really help me a lot. I hope, someday, I will be one of the players in this exciting field! Thanks to Dr. Seyedali Mirjalili."


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 learn optimization
  • Anyone who wants to solve an optimization problem
  • Understand the concepts of stochastic optimization algorithms Analyse and adapt modern optimization algorithms Curated for the Udemy Business collection Requirements You should have basic knowledge of programming You should be familiar with Matlab's built-in programming language Description This is an introductory course to the stochastic optimization problems and algorithms as the basics sub-fields in Artificial Intelligence . We will cover the most fundamental concepts in the field of optimization including metaheuristics and swarm intelligence. By the end of this course, you will be able to identify and implement the main components of an optimization problem. Optimization problems are different, yet there have mostly similar challenges and difficulties such as constraints, multiple objectives, discrete variables, and noises. This course will show you how to tackle each of these difficulties. Most of the lectures come with coding videos. In such videos, the step-by-step process of implementing the optimization algorithms or problems are presented. We have also a number of quizzes and exercises to practice the theoretical knowledge covered in the lectures. Here is the list of topics covered: History of optimization Optimization problems Single-objective optimization algorithms Particle Swarm Optimization Optimization of problems with constraints Optimization of problems with binary and/or discrete variables Optimization of problems with multiple objectives Optimization of problems with uncertainties Particle Swarm Optimization will be the main algorithm, which is a search method that can be easily applied to different applications including Machine Learning, Data Science, Neural Networks, and Deep Learning. I am proud of 200+ 5-star reviews. Some of the reviews are as follows: David said: "This course is one of the best online course I have ever taken. The instructor did an excellent job to very carefully prepare the contents, slides, videos, and explains the complicated code in a very careful way. Hope the instructor can develop much more courses to enrich the society. Thanks!" Khaled said: "Dr. Seyedali is one of the greatest instructor that i had the privilege to take a course with. The course was direct to the point and the lessons are easy to understand and comprehensive. He is very helpful during and out of the course. i truly recommend this course to all who would like to learn optimization\PSO or those who would like to sharpen their understanding in optimization. best of luck to all and THANK YOU Dr. Seyedali." Biswajit said: "This coursework has really been very helpful for me as I have to frequently deal with optimization. The most prominent feature of the course is the emphasis given on coding and visualization of results . Further, the support provided by Dr. Seyedali through personal interaction is top notch. Boumaza said: " Good Course from Dr. Seyedali Mirjalili. It gives us clear picture of the algorithms used in optimization. It covers technical as well as practical aspects of optimization. Step by step and very practical approach to optimization through well though and properly explained topics , highly recommended course You really help me a lot. I hope, someday, I will be one of the players in this exciting field! Thanks to Dr. Seyedali Mirjalili." 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 learn optimization Anyone who wants to solve an optimization problem Show more Show less Featured review Hassan Eid 10 courses 1 review Rating: 5.0 out of 5 2 years ago This Course is what i was searching for in Optimization field, Dr. Ali made optimization simple and easy (piece of cake) for everyone. I would like to thank him as this course will be agreat step towards my master degree. Show more Show less Course content 10 sections • 36 lectures • 8h 3m total length Expand all sections Introduction 2 lectures • 15min Introduction Preview 07:51 History of optimization 06:39 Optimization problems 1 lecture • 12min Optimization problems 11:38 Quiz 1 6 questions Optimization of problems with one objective 2 lectures • 23min Optimization of problems with one objective 11:54 Stochastic Optimization Algorithms 11:15 Quiz 2 9 questions Particle Swarm Optimization (PSO) 4 lectures • 1hr 11min Theory of Particle Swarm Optimization Preview 19:07 Implementing Particle Swarm Optimization in Matlab 33:39 PSO with bounding velocity 12:42 Applying PSO to the table design problem 05:40 Quiz 3 6 questions Optimization of problems with constraints 2 lectures • 25min Constrained optimization 08:02 Coding a constrained objective function and how to solve it in Matlab 17:16 Quiz 4 4 questions Optimization of problems with discrete variables 3 lectures • 35min Discrete optimization 06:01 Coding a binary PSO 13:19 Coding a discrete PSO 15:16 Quiz 5 2 questions Optimization of problems with multiple objectives 2 lectures • 16min Multi-objective optimization 07:42 Multi-objective algorithms 08:20 Quiz 6 4 questions Optimization of problems with uncertainties 3 lectures • 50min Robust optimization 26:36 Coding an expectation measure 14:29 Coding a variance measure 08:49 Quiz 7 3 questions Bonus videos (FAQ) 16 lectures • 3hr 56min How to write PSO as a function? 16:10 How to run an algorithm (PSO) multiple times and collect statistical results? 13:27 How to conduct a statistical test (Wilcoxon ranksum) when comparing algorithms? 14:02 How to compare the convergence curves of two algorithms? 12:19 How to solve maximization problems? 15:49 How to calculate and visualize the average objective of solutions? 11:29 How to visualize the fluctuations of a variable is a solution? 14:11 How to visualize the search history of particles in PSO? 18:55 How to shift and bias a test function? 08:39 How to better visualize qualitative data of PSO? 18:12 How to store the data generated during the optimization process in files? 06:53 How to change the stopping criterion of an optimization algorithm? 14:41 How to frequently update GBEST in PSO? 10:26 How to solve problems with both binary/discrete and continuous variables: PART 1 13:12 How to solve problems with both binary/discrete and continuous variables: PART 2 19:48 How to solve problems with both binary/discrete and continuous variables: PART 3 27:26 Thank You 1 lecture • 2min Thank You: voucher for my other courses 01:41 Instructor Seyedali Mirjalili PhD in Artificial Intelligence 4.4 Instructor Rating 3,044 Reviews 11,397 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 25,000 citations in total with an H-index of 56 . 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. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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  • Analyse and adapt modern optimization algorithms Curated for the Udemy Business collection Requirements You should have basic knowledge of programming You should be familiar with Matlab's built-in programming language Description This is an introductory course to the stochastic optimization problems and algorithms as the basics sub-fields in Artificial Intelligence . We will cover the most fundamental concepts in the field of optimization including metaheuristics and swarm intelligence. By the end of this course, you will be able to identify and implement the main components of an optimization problem. Optimization problems are different, yet there have mostly similar challenges and difficulties such as constraints, multiple objectives, discrete variables, and noises. This course will show you how to tackle each of these difficulties. Most of the lectures come with coding videos. In such videos, the step-by-step process of implementing the optimization algorithms or problems are presented. We have also a number of quizzes and exercises to practice the theoretical knowledge covered in the lectures. Here is the list of topics covered: History of optimization Optimization problems Single-objective optimization algorithms Particle Swarm Optimization Optimization of problems with constraints Optimization of problems with binary and/or discrete variables Optimization of problems with multiple objectives Optimization of problems with uncertainties Particle Swarm Optimization will be the main algorithm, which is a search method that can be easily applied to different applications including Machine Learning, Data Science, Neural Networks, and Deep Learning. I am proud of 200+ 5-star reviews. Some of the reviews are as follows: David said: "This course is one of the best online course I have ever taken. The instructor did an excellent job to very carefully prepare the contents, slides, videos, and explains the complicated code in a very careful way. Hope the instructor can develop much more courses to enrich the society. Thanks!" Khaled said: "Dr. Seyedali is one of the greatest instructor that i had the privilege to take a course with. The course was direct to the point and the lessons are easy to understand and comprehensive. He is very helpful during and out of the course. i truly recommend this course to all who would like to learn optimization\PSO or those who would like to sharpen their understanding in optimization. best of luck to all and THANK YOU Dr. Seyedali." Biswajit said: "This coursework has really been very helpful for me as I have to frequently deal with optimization. The most prominent feature of the course is the emphasis given on coding and visualization of results . Further, the support provided by Dr. Seyedali through personal interaction is top notch. Boumaza said: " Good Course from Dr. Seyedali Mirjalili. It gives us clear picture of the algorithms used in optimization. It covers technical as well as practical aspects of optimization. Step by step and very practical approach to optimization through well though and properly explained topics , highly recommended course You really help me a lot. I hope, someday, I will be one of the players in this exciting field! Thanks to Dr. Seyedali Mirjalili." 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 learn optimization Anyone who wants to solve an optimization problem Show more Show less Featured review Hassan Eid 10 courses 1 review Rating: 5.0 out of 5 2 years ago This Course is what i was searching for in Optimization field, Dr. Ali made optimization simple and easy (piece of cake) for everyone. I would like to thank him as this course will be agreat step towards my master degree. Show more Show less Course content 10 sections • 36 lectures • 8h 3m total length Expand all sections Introduction 2 lectures • 15min Introduction Preview 07:51 History of optimization 06:39 Optimization problems 1 lecture • 12min Optimization problems 11:38 Quiz 1 6 questions Optimization of problems with one objective 2 lectures • 23min Optimization of problems with one objective 11:54 Stochastic Optimization Algorithms 11:15 Quiz 2 9 questions Particle Swarm Optimization (PSO) 4 lectures • 1hr 11min Theory of Particle Swarm Optimization Preview 19:07 Implementing Particle Swarm Optimization in Matlab 33:39 PSO with bounding velocity 12:42 Applying PSO to the table design problem 05:40 Quiz 3 6 questions Optimization of problems with constraints 2 lectures • 25min Constrained optimization 08:02 Coding a constrained objective function and how to solve it in Matlab 17:16 Quiz 4 4 questions Optimization of problems with discrete variables 3 lectures • 35min Discrete optimization 06:01 Coding a binary PSO 13:19 Coding a discrete PSO 15:16 Quiz 5 2 questions Optimization of problems with multiple objectives 2 lectures • 16min Multi-objective optimization 07:42 Multi-objective algorithms 08:20 Quiz 6 4 questions Optimization of problems with uncertainties 3 lectures • 50min Robust optimization 26:36 Coding an expectation measure 14:29 Coding a variance measure 08:49 Quiz 7 3 questions Bonus videos (FAQ) 16 lectures • 3hr 56min How to write PSO as a function? 16:10 How to run an algorithm (PSO) multiple times and collect statistical results? 13:27 How to conduct a statistical test (Wilcoxon ranksum) when comparing algorithms? 14:02 How to compare the convergence curves of two algorithms? 12:19 How to solve maximization problems? 15:49 How to calculate and visualize the average objective of solutions? 11:29 How to visualize the fluctuations of a variable is a solution? 14:11 How to visualize the search history of particles in PSO? 18:55 How to shift and bias a test function? 08:39 How to better visualize qualitative data of PSO? 18:12 How to store the data generated during the optimization process in files? 06:53 How to change the stopping criterion of an optimization algorithm? 14:41 How to frequently update GBEST in PSO? 10:26 How to solve problems with both binary/discrete and continuous variables: PART 1 13:12 How to solve problems with both binary/discrete and continuous variables: PART 2 19:48 How to solve problems with both binary/discrete and continuous variables: PART 3 27:26 Thank You 1 lecture • 2min Thank You: voucher for my other courses 01:41 Instructor Seyedali Mirjalili PhD in Artificial Intelligence 4.4 Instructor Rating 3,044 Reviews 11,397 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 25,000 citations in total with an H-index of 56 . 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. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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