20-12-2012, 06:47 PM
Ant Colony Optimization
1Ant Colony.ppt (Size: 1.48 MB / Downloads: 43)
Ant
An ant is a natural creature . They are blind , the ants follow each other by a chemical called pheromone.
introduction
The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers . An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony, In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food.
Background
Discrete optimization problems difficult to solve
“Soft computing techniques” developed in past ten years:
Genetic algorithms (GAs)
based on natural selection and genetics
Ant Colony Optimization (ACO)
modeling ant colony behavior
Optimization Technique Proposed by Marco Dorigo in the early ’90
Often applied to TSP (Travelling Salesman Problem): shortest path between n nodes
Artificial ant colony system is made from the principle of ant colony system for solving kinds of optimization problems. Pheromone is the key of the decision-making of ants.
Natural ants: How do they do it?
The pheromone concentration on trail B will increase at a higher rate than on A, and soon the ants on route A will choose to follow route B
Since most ants will no longer travel on route A, and since the pheromone is volatile, trail A will start evaporating
Only the shortest route will remain!
ACO System
Starting node selected at random
Path selected at random
based on amount of “trail” present on possible paths from starting node
higher probability for paths with more “trail”
Ant reaches next node, selects next path
Continues until reaches starting node
Finished “tour” is a solution
conclusion
In nut shell an ant colony concept is used in solving the graph problem because an ant is a natural creature. They use natural mechanism to find the shortest path and Destination(food).
Like this in a computational concept the humans use ant concept for finding the shortest path from source to node. Ant system is also used in many applications that are used in computer and real life problems.
Ants have natural behavior but humans use natural phenomena as well as artificial phenomena in intelligence system
1Ant Colony.ppt (Size: 1.48 MB / Downloads: 43)
Ant
An ant is a natural creature . They are blind , the ants follow each other by a chemical called pheromone.
introduction
The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers . An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony, In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food.
Background
Discrete optimization problems difficult to solve
“Soft computing techniques” developed in past ten years:
Genetic algorithms (GAs)
based on natural selection and genetics
Ant Colony Optimization (ACO)
modeling ant colony behavior
Optimization Technique Proposed by Marco Dorigo in the early ’90
Often applied to TSP (Travelling Salesman Problem): shortest path between n nodes
Artificial ant colony system is made from the principle of ant colony system for solving kinds of optimization problems. Pheromone is the key of the decision-making of ants.
Natural ants: How do they do it?
The pheromone concentration on trail B will increase at a higher rate than on A, and soon the ants on route A will choose to follow route B
Since most ants will no longer travel on route A, and since the pheromone is volatile, trail A will start evaporating
Only the shortest route will remain!
ACO System
Starting node selected at random
Path selected at random
based on amount of “trail” present on possible paths from starting node
higher probability for paths with more “trail”
Ant reaches next node, selects next path
Continues until reaches starting node
Finished “tour” is a solution
conclusion
In nut shell an ant colony concept is used in solving the graph problem because an ant is a natural creature. They use natural mechanism to find the shortest path and Destination(food).
Like this in a computational concept the humans use ant concept for finding the shortest path from source to node. Ant system is also used in many applications that are used in computer and real life problems.
Ants have natural behavior but humans use natural phenomena as well as artificial phenomena in intelligence system