chapter six

6 Swarm intelligence: Ants

 

This chapter covers

  • Seeing and understanding what inspired swarm intelligence algorithms
  • Solving problems with swarm intelligence algorithms
  • Designing and implementing an ant colony optimization algorithm

6.1 What is swarm intelligence?

Swarm intelligence algorithms, much like the evolutionary algorithms discussed in Chapter 5, are a family of nature-inspired algorithms. However, while evolutionary algorithms mimic genetic reproduction, swarm intelligence mimics the collective behavior of animals. When we observe the world around us, we see many life forms that are seemingly primitive and unintelligent as individuals, yet exhibit intelligent emergent behavior when acting in groups.

6.2 Problems applicable to ant colony optimization

6.3 Representing state: What do paths and ants look like?

6.4 The ant colony optimization algorithm life cycle

6.4.1 Initialize the pheromone trails

6.4.2 Set up the population of ants

6.4.3 Choose the next visit for each ant

6.4.4 Update the pheromone trails

6.4.5 Update the best solution

6.4.6 Determine the stopping criteria

6.5 Use cases for ant colony optimization algorithms

6.6 Summary of ant colony optimization