What is ant colony optimization technique?
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Real ants lay down pheromones directing each other to resources while exploring their environment.
What is the use of ant colony optimization?
Ant colony optimization (ACO) is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. In ACO, a set of software agents called artificial ants search for good solutions to a given optimization problem.
Who proposed ant colony optimization?
Marco Dorigo
4 Ant Colony Optimization. Ant Colony Optimization (ACO) was introduced in the early 1990s by Marco Dorigo in his PhD thesis; see Dorigo et al. (1999), for an overview.
What is Ant Colony Optimization PDF?
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. Ant colony optimization exploits a similar mechanism for solving optimization problems.
What is ant colony?
The term “ant colony” refers to the collections of workers, reproductive individuals, and brood that live together, cooperate, and treat one another non-aggressively. Often this comprises the genetically related progeny from a single queen, although this is not universal across ants.
What is ant colony system?
The ant colony algorithm is an algorithm for finding optimal paths that is based on the behavior of ants searching for food. At first, the ants wander randomly. Because the ant-colony works on a very dynamic system, the ant colony algorithm works very well in graphs with changing topologies.
How do ant colonies work?
A queen ant and several male ants will leave the original colony on a nuptial flight. When the ants find a suitable spot, they land, discard their wings and begin building a new nest. Eventually, the worker ants will build the colony around the queen who will set about laying eggs so the colony can grow.
Is ant colony optimization a genetic algorithm?
Genetic Algorithms (GAs) were introduced by Holland as a computational analogy of adaptive systems. GAs are search procedures based on the mechanics of natural selection and natural genetics. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies.
Is ant colony Optimization a genetic algorithm?
What happens when ant colonies meet?
When two colonies of the same AA species meet and contact each other they very quickly recognize that they have met up with members of another group. When AA encounter other ant species, on the other hand, they will almost invariably attack and try to kill each ant in that colony.
How deep do ant colonies go?
These mounds are made up of the dirt, sand and other material the ants must remove as they dig the underground tunnels and chambers in which they nest. In fact, most ant colonies stretch deep underground, some even as deep as 25 feet.
What is the advantage of ant colony optimization over genetic?
They have an advantage over simulated annealing and genetic algorithm approaches of similar problems when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time.
How are algorithms used in ant colony optimization?
Algorithm and formulae. In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph.
When does an ant colony become a super colony?
A supercolony occurs when many ant colonies over a large area unite. They still continue to recognize genetic differences in order to mate, but the different colonies within the super colony avoid aggression.
How does continuous orthogonal ant colony ( COAC ) work?
Continuous Orthogonal Ant Colony (COAC) The pheromone deposit mechanism of COAC is to enable ants to search for solutions collaboratively and effectively. By using an orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently, with enhanced global search capability and accuracy.
Why are ants the inspiration for metaheuristic optimization?
Ant behavior was the inspiration for the metaheuristic optimization technique When a colony of ants is confronted with the choice of reaching their food via two different routes of which one is much shorter than the other, their choice is entirely random.