Combinatorial Problems and Ant Colony Optimization Algorithm

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

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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 :)

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.

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.

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.

Thanks :)

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