PPT On ANT COLONY OPTIMIZATION
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ANT COLONY OPTIMIZATION Presentation Transcript:
1.ANT COLONY OPTIMIZATION
2.Basic Definitions
3.Pheromone
A pheromone is a secreted or excreted chemical factor that triggers a social response in members of the same species. Pheromones are chemicals capable of acting outside the body of the secreting individual to impact the behavior of the receiving individual.
Types
Aggregation
function in defense against predators, mate selection, and overcoming host resistance by mass attack.
Alarm
Some species release a volatile substance when attacked by a predator that can trigger aggression (in ants, bees, termites) in members of the same species.
4.Signal
cause short-term changes, such as the neurotransmitter release that activates a response.
Territorial
Laid down in the environment, territorial pheromones mark the boundaries of an organism's territory.
Trail
Trail pheromones are common in social insects. For example, ants mark their paths with these pheromones, which are volatile hydrocarbons.
Information
There is two kind of information:
Heuristic Information
Pheromone trail information
5.Heuristic Information
It related to general strategies or methods for solving problems.
That solve a problem more quickly but is not certain to arrive at an optimal solution.
A heuristic is a word from the Greek meaning "to discover." It is an approach to problem solving that takes one's personal experience into account.
Heuristic techniques involving or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial and error methods.
refers to experience-based techniques for problem solving, learning, and discovery. Where the exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution.
Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, or common sense.
6.Metaheuristics
A metaheuristic is a set of concepts that can be used to define heuristic methods that can be applied to a wide set of different problems.
“A metaheuristics refers to a master strategy that guides and modifies other heuristics to produce solutions beyond those that are normally generated in a quest for local optimality” – Fred Glover and Manuel Laguna
A metaheuristic can be seen as a general algorithmic framework which can be applied to different optimization problems with relatively few modifications to make them adapted to a specific problem.
Examples of metaheuristics include ant colony optimization (ACO), tabu search (TS), iterated local search (ILS), and simulated annealing (SA)
7.There are properties that characterize most metaheuristics:
Metaheuristics are strategies that guide the search process.
The goal is to efficiently explore the search space in order to find near optimal solutions.
Techniques which constitute metaheuristic algorithms range from simple local search procedures to complex learning processes.
Metaheuristic algorithms are approximate and usually non deterministic.
Metaheuristics are not problem-specific.
8.Swarm Intelligence
Swarm intelligence takes inspiration from the social behaviors of insects and of other animals.
In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
9.Ant colony optimization
The ACO is the probability algorithm used for searching optimization paths. It was proposed by Marco Dorigo in his doctoral dissertation in 1992, and the idea was from the activities that ants explore ways when they are looking for food.
10.Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony.
In ACO, a number of artificial ants build solutions to an optimization problem and exchange information on their quality via a communication scheme that is reminiscent of the one adopted by real ants.
The first ant colony optimization algorithm is known as Ant System and was proposed in the early nineties. Since then, several other ACO algorithms have been proposed.
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ANT COLONY OPTIMIZATION Presentation Transcript:
1.ANT COLONY OPTIMIZATION
2.Basic Definitions
3.Pheromone
A pheromone is a secreted or excreted chemical factor that triggers a social response in members of the same species. Pheromones are chemicals capable of acting outside the body of the secreting individual to impact the behavior of the receiving individual.
Types
Aggregation
function in defense against predators, mate selection, and overcoming host resistance by mass attack.
Alarm
Some species release a volatile substance when attacked by a predator that can trigger aggression (in ants, bees, termites) in members of the same species.
4.Signal
cause short-term changes, such as the neurotransmitter release that activates a response.
Territorial
Laid down in the environment, territorial pheromones mark the boundaries of an organism's territory.
Trail
Trail pheromones are common in social insects. For example, ants mark their paths with these pheromones, which are volatile hydrocarbons.
Information
There is two kind of information:
Heuristic Information
Pheromone trail information
5.Heuristic Information
It related to general strategies or methods for solving problems.
That solve a problem more quickly but is not certain to arrive at an optimal solution.
A heuristic is a word from the Greek meaning "to discover." It is an approach to problem solving that takes one's personal experience into account.
Heuristic techniques involving or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial and error methods.
refers to experience-based techniques for problem solving, learning, and discovery. Where the exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution.
Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, or common sense.
6.Metaheuristics
A metaheuristic is a set of concepts that can be used to define heuristic methods that can be applied to a wide set of different problems.
“A metaheuristics refers to a master strategy that guides and modifies other heuristics to produce solutions beyond those that are normally generated in a quest for local optimality” – Fred Glover and Manuel Laguna
A metaheuristic can be seen as a general algorithmic framework which can be applied to different optimization problems with relatively few modifications to make them adapted to a specific problem.
Examples of metaheuristics include ant colony optimization (ACO), tabu search (TS), iterated local search (ILS), and simulated annealing (SA)
7.There are properties that characterize most metaheuristics:
Metaheuristics are strategies that guide the search process.
The goal is to efficiently explore the search space in order to find near optimal solutions.
Techniques which constitute metaheuristic algorithms range from simple local search procedures to complex learning processes.
Metaheuristic algorithms are approximate and usually non deterministic.
Metaheuristics are not problem-specific.
8.Swarm Intelligence
Swarm intelligence takes inspiration from the social behaviors of insects and of other animals.
In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
9.Ant colony optimization
The ACO is the probability algorithm used for searching optimization paths. It was proposed by Marco Dorigo in his doctoral dissertation in 1992, and the idea was from the activities that ants explore ways when they are looking for food.
10.Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony.
In ACO, a number of artificial ants build solutions to an optimization problem and exchange information on their quality via a communication scheme that is reminiscent of the one adopted by real ants.
The first ant colony optimization algorithm is known as Ant System and was proposed in the early nineties. Since then, several other ACO algorithms have been proposed.
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