27-09-2012, 11:17 AM
SWARM INTELLIGENCE
1SWARM.doc (Size: 3.2 KB / Downloads: 22)
ABSTRACT
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. The characterizing property of a swarm intelligence system is its ability to act in a coordinated way without the presence of a coordinator or of an external controller. 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. This is inspired by the intelligent behaviour seen in swarms of animals such as a colony of of ants, flocks of birds or schools of fish,bacterial growth.As SI systems are inspired by natural biological swarms, standard algorithms are based on the search for food. The differences in food searching techniques lead to different SI algorithms, including:Ant Colony Optimisation (ACO) replicate the natural behaviour of ants. Ants will randomly spread out and search for food. When food is discovered an ant will return to its base leaving a pheromone trail. Upon finding a pheromone trail another ant will follow that train and if it finds food on this trail it too will return to base, leaving its own pheromone trail. If an ant is on a pheromone trail and crosses a stronger pheromone trail it will follow the stronger trail. Pheromones decay over time allowing the removal of non optimal solutions. The ACO algorithm finds optimal solutions because shorter paths are traveled over faster and hence more often quickly leading to strong pheromone trails. Introducing new ants randomly over time allows responses to dynamic changes in the environment. ACO is typically used to find an optimal path.Particle Swarm Optimisation (PSO)This form is based on schools of fish and flocks of birds finding food. PSO is used to find an optimal point in space.Agents begin by being randomly spread out in the environment with random velocities. As the agents move they examine the area around them and communicate with the other agents their evaluations. This communication can either be a global communication or a local ‘neighbourhood’ communication. Based on their own findings and the findings communicated to them, agents will adjust their velocities to follow better solutions. As a result agents will begin to head into areas where the best solutions are being found and this leads to an optimal solution.
1SWARM.doc (Size: 3.2 KB / Downloads: 22)
ABSTRACT
Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. The characterizing property of a swarm intelligence system is its ability to act in a coordinated way without the presence of a coordinator or of an external controller. 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. This is inspired by the intelligent behaviour seen in swarms of animals such as a colony of of ants, flocks of birds or schools of fish,bacterial growth.As SI systems are inspired by natural biological swarms, standard algorithms are based on the search for food. The differences in food searching techniques lead to different SI algorithms, including:Ant Colony Optimisation (ACO) replicate the natural behaviour of ants. Ants will randomly spread out and search for food. When food is discovered an ant will return to its base leaving a pheromone trail. Upon finding a pheromone trail another ant will follow that train and if it finds food on this trail it too will return to base, leaving its own pheromone trail. If an ant is on a pheromone trail and crosses a stronger pheromone trail it will follow the stronger trail. Pheromones decay over time allowing the removal of non optimal solutions. The ACO algorithm finds optimal solutions because shorter paths are traveled over faster and hence more often quickly leading to strong pheromone trails. Introducing new ants randomly over time allows responses to dynamic changes in the environment. ACO is typically used to find an optimal path.Particle Swarm Optimisation (PSO)This form is based on schools of fish and flocks of birds finding food. PSO is used to find an optimal point in space.Agents begin by being randomly spread out in the environment with random velocities. As the agents move they examine the area around them and communicate with the other agents their evaluations. This communication can either be a global communication or a local ‘neighbourhood’ communication. Based on their own findings and the findings communicated to them, agents will adjust their velocities to follow better solutions. As a result agents will begin to head into areas where the best solutions are being found and this leads to an optimal solution.