06-07-2012, 03:34 PM
swarm intelligence
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ABSTRACT:
Swarm intelligence (SI) describes the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.
INTRODUCTION:
SI is the property of a system whereby the collective behaviors of agents interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore distributed problem solving without centralized control or the provision of a global model. One of the cores tenets of SI work is that often a decentralized, bottom-up approach to controlling a system is much more effective than traditional, centralized approach. Groups performing tasks effectively by using only a small set of rules for individual behaviour is called swarm intelligence.
MODELLING SWARM BEHAVIOUR
The simplest mathematical models of animal swarms generally represent individual animals as following three rules:
1. Move in the same direction as your neighbor
2. Remain close to your neighbors
3. Avoid collisions with your neighbors
Many current models use variations on these rules, often implementing them by means of concentric "zones" around each animal. In the zone of repulsion, very close to the animal, the focal animal will seek to distance itself from its neighbors to avoid collision. Slightly further away, in the zone of alignment, the focal animal will seek to align its direction of motion with its neighbors.
STOCHASTIC DIFFUSION SEARCH
It belongs to a family of swarm intelligence and naturally inspired search and optimisation algorithms which includes ant colony optimization, particle swarm optimization and genetic algorithms. It is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterized by the agent's current hypothesis.
FLOCKING AND SCHOOLING IN BIRDS AND FISH
Scientists have shown that these elegant swarm-level behaviors can be understood as the result of a self-organized process where no leader is in charge and each individual bases its movement decisions solely on locally available information: the distance, perceived speed, and direction of movement of neighbours. These studies have inspired a number of computer simulations that are now used in the computer graphics industry for the realistic reproduction of flocking in movies and computer games.