Combinatorial Problems and Ant Colony Optimization Algorithm
 5 hours ondemand video
 10 downloadable resources
 Full lifetime access
 Access on mobile and TV
 Certificate of Completion
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Try Udemy for Business Formulate combinatorial optimization problems

Solve combinatorial optimization problems

Develop and use Ant Colony Optimization
 Solve Travelling Salesman Problem
 Basics coding skills in Matlab
Search methods and heuristics are of the most fundamental Artificial Intelligence techniques. One of the most wellregarded 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. Bruteforce (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.
 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
This lesson gives you some tips on how to get most of this course. It shows you how to download Matlab and SciLab too.
This lesson allows you to learn how different combinatorial problems are as compared to other problems. Some case studies are covered too.
Bruteforce (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.
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.
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.
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.