Combinatorial Problems and Ant Colony Optimization Algorithm
4.6 (55 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
355 students enrolled

Combinatorial Problems and Ant Colony Optimization Algorithm

Let's learn Artificial Intelligence search methods: optimization, exact algorithms, heuristics, and metaheuristics
4.6 (55 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
355 students enrolled
Last updated 10/2018
English
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Current price: $9.99 Original price: $199.99 Discount: 95% off
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This course includes
  • 5 hours on-demand video
  • 10 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Formulate combinatorial optimization problems
  • Solve combinatorial optimization problems

  • Develop and use Ant Colony Optimization

  • Solve Travelling Salesman Problem
Requirements
  • Basics coding skills in Matlab
Description

Search methods and heuristics are of the most fundamental Artificial Intelligence techniques. One of the most well-regarded of them is Ant Colony Optimization that allows humans to solve some of the most challenging problems in the history. This course takes you through the details of this algorithm. The course is helpful to learn the following concepts:


Part 1:


1. The main components of the

2. Formulating combinatorial optimization problems

3. Difficulty of combinatorial optimization problems

4. State space tree

5. Search space

6. Travelling Salesman Problem (TSP)


Part 2:


1. Exact methods

2. Heuristics methods

3. Brute-force (exhaustive) algorithm to solve combinatorial problems

4. Branch and bound algorithm to solve combinatorial problems

5. Nearest neighbour to solve Travelling Salesman Problem


Part 3:


1. Inspirations of the Ant Colony Optimization (ACO)

2. Mathematical models of the Ant Colony Optimization 

3. Implementation of the Ant Colony Optimization 

4. Testing and analysing the performance of the Ant Colony Optimization 

5. Tuning the parameter of the Ant Colony Optimization 



Ant Colony 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.



Some of the reviews are as follows: 

Fan said: "Another Wonderful course of Dr Seyedali,I really appreciate it! I also look forward to more applications and examples of ACO."


Ashish said: "This course clears my concept about Ant colony optimization speciaally with MATLAB and how to apply to our problem. Thank you so much, Sir, for design such a helpful course"


Join 100+ 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 about combinatorial optimization and discrete mathematics
  • Anyone who wants to understand different combinatorial optimization algorithms
  • Anyone who wants to understand and implement Ant Colony Optimization
  • Anyone who wants to solve Travelling Salesman Problem
Course content
Expand all 19 lectures 04:57:38
+ Introduction
1 lecture 05:22

This lesson gives you some tips on how to get most of this course. It shows you how to download Matlab and SciLab too.

Preview 05:22
+ Combinatorial Optimization Problems
3 lectures 53:14

This lesson allows you to learn how different combinatorial problems are as compared to other problems. Some case studies are covered too.

Preview 18:19

This lesson shows how difficult combinatorial optimization problems are. They are one of the most difficult problems to solve, and you will see why in this lesson. The exponential growth of these problems are discussed too.

Difficulty of Combinatorial Optimization Problems
20:21

State space tree is one of the most well-known tools when solving combinatorial problems. In this lesson, you will learn how to draw and use state space tress in detail.

Sate Space of Combinatorial Optimization Problems
14:34
+ Combinatorial Optimization Algorithms
4 lectures 42:10
The Main Classes of Combinatorial Optimization Algorithms
20:09

Brute-force (often called exhaustive) search methods are the easiest yet computationally expensive problem solving techniques. In this lesson, you will learn how this type of search works. An example is also given that shows how to traverse a state space tree and find the shortest path from the root to the leaves.

Brute-Force Search
06:38

One of the efficient search methods is branch and bound. This video shows you how to use this method for finding the shortest path in a state space tree.

Branch and Bound Method
08:38

Complete search algorithms are computationally expensive and less practical for large-scale problems. This lesson shows you a heuristic (informed) search method called nearest neighbour algorithm. The nearest neighbour is applied to a state space tree too.

Nearest Neighbours Method
06:45
+ Ant Colony Optimization
10 lectures 03:15:11

This lesson covers the main inspiration of the Ant Colony Optimization. Stigmergy is first discussed with an intuitive analogy. Several examples of how ants find the shortest path from a nest to a food source are given too.

Preview 19:19

Do you hate complicated mathematical equations? I hate them too :D 

So this is why I tried to simplify the mathematical models of the Ant Colony Optimization in this video. This lesson shows the mathematical models of ACO in the easiest possible way.

Mathematical Models of Ant Colony Optimization Algorithm
22:10

This video uses Matlab programming language to set up a framework to develop the Ant Colony Optimization (ACO).

A framework for implementing ACO
14:45

This lesson takes you through the steps of preparing and implementing a combinatorial problem in Matlab programming language.

Preparing the problem
09:55

In this video, you will learn how to draw a graph for Travelling Salesman Problem (TSP) in Matlab to solve it later on using the Ant Colony Optimization.

Drawing the graph
14:17

Ant Colony Optimization has several controlling parameters. This video shows you the steps of writing them in Matlab. It also shows you the steps of implementing and coding the initial colony of ants and a roulette wheel.

Initial Paramters, creating a colony, and roulette wheel
36:54

This lesson shows you how to find the best ant in the ant colony implemented in the last video. I named it Queen since it is the best solution for the problem :)

Finding the queen!
13:07

The pheromone matrix and evaporation are the main mechanisms of Ant Colony Optimization (ACO). This videos shows you how to implement these mechanisms in Matlab.

Updating the pheromone matrix and apply evaporation
18:54

Data visualization is essential in the field of Artificial Intelligence (AI). This vide shows you the best tools and techniques to visualize the main mechanisms and results of the Ant Colony Optimization.

Visualizing the best tour and phromone concentraiton
30:23

Ant Colony Optimization (ACO) has several parameters that have significant impact of its performance. Therefore, tuning them should be done carefully. This video demonstrates the impact of these parameters and suggest the best values.

Preview 15:27
+ Bonus Vidoes
1 lecture 01:41

Thanks :)

You can use this coupon code to enrol my other courses: THANKYOUSOMUCH 

Thank You
01:41