Swarm Intelligence Algorithms#


  1. Particle Swarm Optimization (PSO)

  2. Ant Colony Optimization (ACO)

  3. Artificial Bee Colony (ABC)

  4. Firefly Algorithm (FA)

The social insect societies as decentralized organizations are based on the cooperation of separate, simple, and somewhat random units, distributed in the environment, who work without supervision, having only access to local information [8]. In such systems, problems are collectively solved. The theory of self-organization in animal societies shows that very simple, but numerous, interactions taking place between individuals may ensure complex performances and produce Collective Intelligence (CI) at the level of the group [9].
Swarm intelligence (SI) or collective intelligence refers to the phenomenon of a system of spatially distributed individuals coordinating their actions in a decentralized and self-organized manner so as to exhibit complex collective behavior. Swarm Intelligence systems are typically made up of a population of simple agents interacting locally with one another and with their environment. This interaction often leads to the emergence of global behavior.

Key elements of SI include:

  • A large number of “simple” processing elements work without supervision

  • Neighbourhood communication

  • Though convergence is guaranteed, the time to convergence is uncertain

A typical SI-based Algorithm adopts a pattern as follows:

  1. Initialize parameters.

  2. Initialize population.

  3. While an end condition has not yet been reached:

    • Find best so far

    • Find best neighbour

    • Update each individual

Examples of Swarm Intelligence Algorithms#

  • Particle Swarm Optimization (PSO)

  • Ant Colony Optimization (ACO)

  • Artificial Bee Colony (ABC)

  • Firefly Algorithm (FA)

  • Bat Algorithm (BA)

  • Grey wolf optimizer

  • Ant lion optimizer

  • Artifical algae algorithm

  • Chicken swarm optimization

  • Artifical fish-swarm algorithm

  • Alliance Algorithm (AA)