Ant, Bee, and Swarm Particle Algorithms

Ant Colony Optimization (ACO)
When ants are searching for food they release pheromones along their routes to guide other ants to the food source. The stronger the pheromone trail the more ants that will follow it and make the trail stronger. As time passes pheromones will disappear and the shortest route will be the only route left.
Example of games that use this algorithm;
    Tetris,
     and an optimization of Pacman, Ms Pacman.

Artificial Bee Colony (ABC)
Scout bees search random flower patches and return to the hive where they evaluate the patches they visited by certain qualities. While at the hive they perform the “waggle dance” which tells the rest of the hive the location of the food source. The onlookers determine what food source they will collect from based on profitability. When they are finished with that food source the bee then scouts for other sources.
Implemented example games;
    Sudoku,
    The Hexxagon Game.

Particle Swarm Optimization (PSO)
This algorithm was based on birds seeking food, where there is a piece of food and many birds in that area. All of the birds know their distance from the food and the birds closest to the food.
Game examples;
    TicTacToe,
    Checkers.