Swarm Intelligence

swarmic painting

ant routing

Swarm Intelligence (SI) refers to numerous, simple units working in concert to solve complex problems. It is field in computer science and artificial intelligence based on examples from nature such as an ant colony, made of many animals that communicate with each other to achieve unified goals. In computer models the ‘animals,’ or individual units, are called ‘agents.’ Swarm intelligence emerges from decentralized, self-organizing systems, natural or artificial. The expression was introduced by electrical engineers Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. The application of swarm principles to robots is called ‘swarm robotics,’ while ‘swarm intelligence’ refers to the more general set of algorithms. ‘Swarm prediction’ has been used in the context of forecasting problems.

SI systems consist typically of a population of simple agents or ‘boids’ (named for a 1986 artificial life program that simulates the flocking behavior of birds) interacting locally with one another and with their environment. A number of natural systems have been used as models (e.g. animal herding, bacterial growth, fish schooling and microbial intelligence). The agents follow very simple rules, and although there is no centralized control structure dictating how each unit should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of ‘intelligent’ global behavior, unknown to the individuals.

Ant colony optimization (ACO), introduced by artificial intelligence researcher Marco Dorigo in his doctoral dissertation, is a class of optimization algorithms modeled on the actions of an ant colony. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs. Artificial ‘ants’—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Natural ants lay down pheromones directing each other to resources while exploring their environment. The simulated ‘ants’ similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions.

Artificial bee colony algorithm (ABC) is a meta-heuristic algorithm introduced by Turkish computer scientist Dervis Karaboga in 2005 that simulates the foraging behavior of honey bees. The ABC algorithm has three phases: employed bee, onlooker bee and scout bee. In the employed bee and the onlooker bee phases, bees exploit the sources by local searches in the neighborhood of the solutions selected based on deterministic selection in the employed bee phase and the probabilistic selection in the onlooker bee phase. In the scout bee phase which is an analogy of abandoning exhausted food sources in the foraging process, solutions that are not beneficial anymore for search progress are abandoned, and new solutions are inserted instead of them to explore new regions in the search space. The algorithm has a well-balanced exploration and exploitation ability.

The Gravitational search algorithm (GSA) is based on the law of gravity and the notion of mass interactions. It bases its model on Newtonian physics, with collection of masses serving as searcher agents. Using the gravitational force, every mass in the system can see the situation of other masses. The gravitational force is therefore a way of transferring information between different masses. The heavy masses correspond to good solutions of the problem. River Formation Dynamics (RFD) is based on how water forms rivers by eroding the ground and depositing sediments. After drops transform the landscape by increasing/decreasing the altitude of places, solutions are given in the form of paths of decreasing altitudes. Decreasing gradients are constructed, and these gradients are followed by subsequent drops to compose new gradients and reinforce the best ones.


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