Combinatorial Problems and Ant Colony Optimization Algorithm

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 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. The nearest neighbour to solve the 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 specially 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.