This chapter covers
- Getting familiar with ant colony optimization metaheuristics
- Understanding different variants of ant colony optimization
- Understanding artificial bee colony
- Applying these swarm intelligence algorithms to solve continuous and discrete optimization problems
In the previous chapter, we looked at the particle swarm optimization (PSO) algorithm, but ant colony optimization (ACO) and artificial bee colony (ABC) are other widely used swarm intelligence algorithms, drawing inspiration from ants and bees to tackle diverse optimization problems. Let’s revisit the treasure hunting mission and assume you still want to follow a cooperative and iterative approach to find the treasure (which is the best solution in the case of an optimization problem). You and your friends are divided into two groups: the ant group and the bee group. Each group has its own unique way of finding the treasure, using ant colony optimization or the artificial bee colony algorithm. You can join either of these two groups.