Evolutionary Computing Classification - Swarm Intelligence Optimization

Swarm Intelligence Optimization (SO): SO is a class of optimization algorithms inspired by the behavior of social animals, such as birds, ants, and bees. They work by representing candidate solutions as particles and then using interaction and cooperation between these particles to find a better solution. some of the most widely used families of algorithms in SO include:

  1. Ant Colony Optimization (ACO). Ant Colony Optimization is a swarm intelligence optimization algorithm inspired by the foraging behavior of ants. In ACO, candidate solutions to a problem are represented as ants that explore the search space and update a pheromone trail that guides the movement of other ants. The ants update the pheromone trail based on the quality of the solutions they find, with better solutions leading to stronger pheromone trails.

  2. Particle Swarm Optimization (PSO). Particle Swarm Optimization is a swarm intelligence optimization algorithm that mimics the behavior of social animals, such as birds and fish, to find optimal solutions to a problem. In PSO, candidate solutions are represented as particles that move and interact with each other in a search space. The movement of these particles is guided by local and global information about the search space.

  3. Artificial Bee Colony (ABC). Artificial Bee Colony is a swarm intelligence optimization algorithm inspired by the foraging behavior of honey bees. In ABC, candidate solutions to a problem are represented as bees that explore the search space and update their positions based on the quality of the solutions they find. A combination of local and global information about the search space guides the movement of the bees.

  4. Firefly Algorithm (FA). Firefly Algorithm is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies. In FA, candidate solutions to a problem are represented as fireflies that emit light and update their positions based on the quality of the solutions they find. The fireflies’ movement is guided by their relative brightness, with brighter fireflies attracting other fireflies to their location.

  5. Cuckoo Search (CS): Cuckoo Search is a swarm intelligence optimization algorithm that is inspired by the egg-laying behavior of cuckoos. In CS, candidate solutions to a problem are represented as cuckoos that lay eggs in nests and update their positions based on the quality of the solutions they find. The movement of the cuckoos is guided by a combination of local and global information about the search space.

In summary, all these algorithms are used for optimization but use different strategies to find the optimal solution. Evolutionary algorithms use a genetic metaphor to evolve a population of candidate solutions, and swarm optimization uses a population of agents that interact with each other.