Introduction to the Applications of Ant Colony Optimization

The hard-working ants can teach us a lot about how to find the most efficient roadmap to a problem. Scientifically, this process is known as ‘Ant Colony Optimization’ that involves solving complex optimization problems by immitating ant behavior. Applications of ant colony optimization include graph coloring, mobile robot path planning, feature selection, and more. 

We, at Oodles, as an evolving Machine Learning Development Company, explore how ant colony optimization can support innovative and resilient application development.

Learning from the ants

Ever wondered how ants in ant colonies are so well-aligned with each other? Pheromones are partly responsible for that. Ants secrete chemicals, known as pheromones, through which they communicate with each other. 

Walking down a path, an ant secretes pheromones. Now other ants just follow wherever they find the most amount of pheromone. This is a very smart way of making sure that path an ant chooses is the path leading to the right direction, as more pheromone means more number of ants have traveled that path. There is another interesting property of pheromone - it evaporates with time. This property makes sure the path chosen by the ant is the shortest one.

We can apply this interesting principle to solve computational problems involving graphs. Algorithms that use this principle are known as Ant Colony Algorithms (AOC). Such algorithms use a probabilistic approach to find the solution to a given problem. Like many other algorithms, these algorithms make use of feedback mechanisms for the discovery of a good solution. 

Ant Colony Optimization metaheuristic

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The Ant Colony Optimization metaheuristics consist of (a) initialization of parameters, pheromones trails, and (b) scheduling activities construct. Overall, the scheduling activities comprises of three major algorithms - 

1) ConstructAntSolutions

2) DaemonActions (optional) and

3) UpdatePheromones. 

The scheduling activities construct terminates itself upon satisfying criteria. This construct does not specify the synchronization of these three algorithms. 

ConstuctAntSolution - This algorithm constructs solutions by all ants. 

DaemonActions - It improves the constructed solutions by making use of local searches. This algorithm is optional.

UpdatePheromones - It updates the pheromone level for each solution. It increases the pheromones level for good solutions and decreases the pheromones level for bad ones.

Applications of Ant Colony Optimization

Let’s talk about some of the applications:-

1. Graph Coloring
2. Constraint Satisfaction
3. Scheduling
4. Travelling Salesman Problem

It is observed that these algorithms work best for Travelling Salesman Problem when the cities are less than 75. 

Conclusion 

We can learn a lot from our surroundings. A simple communication technique used by the ants has led us to such an interesting solution. There are many other solutions inspired by real-life like Genetic Algorithms which is based on how evolution works.

With Oodles’ AI developers, harness the power of ant colony optimization to deploy dynamic solutions combined with our machine learning and predictive analytics services.

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