Multi-Objective Optimization with Python Bootcamp A-Z

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Navid Shirzadi

3:43:13

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1. Introduction

1. Course Content.mp4

04:48

2. Course Information.mp4

03:34

2. Before We Start (Course Requirements)

1. Introduction to Google Colab Environment.mp4

09:10

2. NumPy Crash Course.mp4

21:57

3. Genetic Algorithm Concept Biological.mp4

09:24

4. Genetic Algorithm Concept - Mathematical.mp4

07:40

5.1 Exercise Solution.zip

5.2 Source Code.zip

5. Source Codes.html

3. Multi-objective Optimization

1. Introduction to Multi-objective Optimization.mp4

07:19

2. Introduction to pymoo Library.mp4

03:30

4. Finding a Solution Set

1. Problem Formulation.mp4

04:30

2. pymoo Installation.mp4

04:57

3. Class Development.mp4

28:58

4. Initializing an Algorithms.mp4

22:11

5. Optimization Process.mp4

17:54

6. Results & Visualization.mp4

06:13

5. Multi-Criteria Decision Making (MCDM)

1. What is Pareto Front.mp4

08:18

2. What is Multi-Criteria Decision Making (MCDM).mp4

02:42

3. Introduction to Compromise Programming.mp4

05:23

4. Introduction to Pseudo-Weights.mp4

04:12

5. Normalization (Ideal Point and Nadir Point).mp4

16:05

6. Decision Making Using Compromise Programming.mp4

16:42

7. Decision Making Using Pseudo-Weights Method.mp4

09:16

6. Summary and Exercise

1.1 Exercise Solution.zip

1. Exercise.mp4

08:30

Description

Mastering Multi-Objective Optimization and Decision-Making with pymoo: Balancing Objectives, Finding Solutions

What You'll Learn?

Fundamentals of Multi-Objective Optimization (MOO)
pymoo Library and How to Solve a MOO problem in Python
Multi-Objective Optimization Algorithms and How to Initialize them
Multi-Criteria Decision-Making Methods
Compromise Programming
Pseudo-Weights Method

Who is this for?

Students, Researchers a Engineers

Professionals and Practitioners

Data Scientists and Analysts

Entrepreneurs

Enthusiasts and Learners

Decision-Makers

Academic Instructors

Curriculum Developers

What You Need to Know?

Basic Programming Skills: Students should have a fundamental understanding of programming in Python.

Optimization Basics: While not mandatory, some familiarity with optimization concepts and terminology

Object-Oriented Programming (OOP): Understanding the basics of object-oriented programming will be valuable

More details

Description
Course Description:

Welcome to "Multi-Objective Optimization with Python Bootcamp A-Z" In this comprehensive course, you will embark on a journey to become a skilled optimizer, equipped with the knowledge and tools to solve complex problems that involve conflicting objectives. With a focus on using the powerful Pymoo library in the Python environment, you will gain a deep understanding of multi-objective optimization techniques and strategies for making informed decisions.

Course Highlights:

Foundation of Multi-Objective Optimization: Understand the fundamentals of multi-objective optimization, Pareto optimality, and the challenges posed by conflicting objectives.

Optimization Algorithms: Explore a wide range of state-of-the-art algorithms, including genetic algorithms implemented using Pymoo.

Pymoo Library Mastery: Dive deep into the Pymoo library, from installation to customizing algorithms and interpreting results, maximizing your proficiency in multi-objective optimization.

Multi-Criteria Decision Making: Discover methods like Pseudo-Weights and Compromise Programming to make informed decisions while considering multiple criteria.

Real-World Applications: Apply your skills to practical case studies from various domains, learning how to address real challenges with optimization solutions.

Hands-On Projects: Work on hands-on coding exercises and assignments that reinforce your understanding and provide practical experience in solving multi-objective problems.

Visualization and Analysis: Utilize visualization tools to analyze Pareto fronts, trade-offs, and the impact of decision-making methods.

Problem-Solving Strategies: Develop strategies to tackle complex optimization problems, handling constraints, and achieving the right balance between convergence and diversity.

Whether you're a student, researcher, data scientist, or professional, this course offers valuable insights into multi-objective optimization and decision-making. By the end, you'll be equipped to approach complex challenges with confidence, optimize solutions effectively, and make well-informed decisions using the power of Pymoo.

Join us on this exciting journey of Multi-Objective Optimization with Python Bootcamp A-Z. Enroll today and unlock a new dimension of problem-solving expertise.
Who this course is for:
Students, Researchers a Engineers
Professionals and Practitioners
Data Scientists and Analysts
Entrepreneurs
Enthusiasts and Learners
Decision-Makers
Academic Instructors
Curriculum Developers

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My name is Navid Shirzaid and I am super excited that you are here to read this section!I am a researcher with more than 7 years of experience in the field of controlling integrated energy systems with extensive skill in using mathematical optimization strategies. I am also proficient in coding with Python and developing machine learning and deep learning models for different applications. I have several publications in the field of designing and control strategies of energy systems using machine learning, deep learning, and artificial intelligence.To Conclude, I am passionate about Data Science and Machine Learning, and Optimization applications in real-world problems and I really like to share my experience with you!

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