Advanced Intelligent Optimization and its Applications

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Advanced Intelligent Optimization and its Applications

This course is part of Applied AI for Engineers and Scientists: Practitioners Specialization

Instructors: Bo Liu

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Implement optimization algorithms using LLM-assisted Python
Solve engineering and scientific problems with AI techniques

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This course is part of the Applied AI for Engineers and Scientists: Practitioners Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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There are 3 modules in this course

The second course introduces advanced evolutionary computation methods for intelligent optimization and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of genetic algorithms and particle swarm optimization. In this course, the configuration of intelligent optimization algorithms, handling constraints and multiple objectives, as well as combinatorial optimization, is introduced. Since almost all design problems in science and engineering can be formulated into optimization problems, after this course, you will become a professional designer armed with AI techniques, which is a big advantage compared to traditional experience-driven manual design techniques. This skill can be transferred to various domains in science and engineering.

In the Applied AI for Engineers and Scientists: Foundations Specialization, you were introduced to intelligent optimization algorithms such as genetic algorithms and particle swarm optimization. In practical applications, the successful use of these methods depends heavily on the proper configuration of their key hyperparameters. This module examines how such parameter settings influence optimization performance and how to select them effectively in real-world science and engineering contexts. After learning this module, you will be able to:

What's included

12 videos7 readings1 assignment1 ungraded lab

12 videosTotal 113 minutes

Welcome to Advanced Intelligent Optimization and its Applications2 minutes
Review of Genetic Algorithm and Its Python Implementation14 minutes
Tutorial 1 - Genetic Algorithm Walkthrough17 minutes
Review of PSO Algorithm and its Python Implementation9 minutes
Tutorial 2 - PSO Walkthrough7 minutes
GA Configuration - Premature Convergence and Population Diversity13 minutes
GA Parameter Settings (Part 1) - Stopping Criterion7 minutes
GA Parameter Settings (Part 2) - Search Range, Initial Population Range, and Size11 minutes
GA Parameter Settings (Part 3): Crossover and Mutation11 minutes
Parameter Settings in PSO8 minutes
Tutorial - Array Antenna Optimization Using GA13 minutes
Module 4 Recap1 minute

7 readingsTotal 60 minutes

Course Sample Certificate5 minutes
Connect with UofG Online5 minutes
PPT Lecture Slides - Evolutionary Algorithms10 minutes
PPT Lecture Slides - Genetic Algorithm Configuration10 minutes
PPT Lecture Slides - PSO Configuration10 minutes
PPT Lecture Slides - Array Antenna Case Study10 minutes
Module 4 Practice Assignment10 minutes

1 assignmentTotal 30 minutes

Module 4 Quiz30 minutes

1 ungraded labTotal 60 minutes

Assignment 4.1: Optimise a Wind Turbine Blade with GA60 minutes

This module introduces the concepts of constrained and multiobjective optimization, as well as the state-of-the-art algorithms for solving them. Python implementation, assisted by an LLM, is also taught alongside real-world case studies. After learning this module, students will be able to:

What's included

10 videos3 readings1 assignment2 ungraded labs

10 videosTotal 90 minutes

Module 5 Introduction1 minute
Foundations of Constrained Optimization17 minutes
Python Implementation for Constrained Optimization7 minutes
Tutorial 1 - Constrained Optimization Case Study20 minutes
Foundations of Multiobjective Optimization7 minutes
Multiobjective Evolutionary Algorithms12 minutes
The MOEA/D Algorithm15 minutes
Python Implementation of MOEA/D for solving ZDT1 problem3 minutes
Tutorial 2 - MOEA/D Case Study8 minutes
Module 5 Recap1 minute

3 readingsTotal 50 minutes

PPT Lecture Slides - Constrained Optimization10 minutes
PPT Lecture Slides - Multiobjective Optimization10 minutes
Module 5 Practice Assignment30 minutes

1 assignmentTotal 30 minutes

Module 5 Quiz30 minutes

2 ungraded labsTotal 120 minutes

Assignment 5.1: Constrained Optimization for Heat Sink Geometry60 minutes
Assignment 5.2: Multiobjective Optimization of a Heat Sink (MOEA/D)60 minutes

This module introduces combinatorial optimization and the algorithms to solve it. Real-world case studies and Python implementation assisted by LLM are also taught. After learning this module, students will be able to:

What's included

9 videos5 readings1 assignment2 ungraded labs

9 videosTotal 73 minutes

Module 6 Introduction1 minute
Introduction to Combinatorial Optimization9 minutes
GA for 0-1 Combinatorial Optimization12 minutes
Python Implementation: GA for 0-1 Combinatorial Optimization2 minutes
Tutorial 1 - GA for 0-1 Combinatorial Optimization15 minutes
GA for Sequential Combinatorial Optimization9 minutes
Python Implementation: GA for Sequential Combinatorial Optimization2 minutes
Tutorial 2 - GA for Sequential Combinatorial Optimization21 minutes
Module 6 Recap1 minute

5 readingsTotal 50 minutes

PPT Lecture Slides - Intro to Combinatorial Optimization10 minutes
PPT Lecture Slides - 0-1 Combinatorial Optimization10 minutes
PPT Lecture Slides - Sequential Combinatorial Optimization10 minutes
Module 6 Practice Assignment10 minutes
Continue Learning with UofG Online10 minutes

1 assignmentTotal 30 minutes

Module 6 Quiz30 minutes

2 ungraded labsTotal 120 minutes

Assignment 6.1: Solve the Set Cover Problem60 minutes
Assignment 6.2: Quadratic Assignment Problem60 minutes

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Instructors

Bo Liu

University of Glasgow

6 Courses4,242 learners

Offered by

University of Glasgow

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