Ant colony optimization algorithms
From a broader perspective, ACO performs a model-based search[8] and shares some similarities with estimation of distribution algorithms.
In the natural world, ants of some species (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails.
Anthropocentric concepts have been known to lead to the production of IT systems in which data processing, control units and calculating power are centralized.
Ambient networks of intelligent objects and, sooner or later, a new generation of information systems that are even more diffused and based on nanotechnology, will profoundly change this concept.
However, once those objects are interconnected they develop a form of intelligence that can be compared to a colony of ants or bees.
In the case of certain problems, this type of intelligence can be superior to the reasoning of a centralized system similar to the brain.
[11] Nature offers several examples of how minuscule organisms, if they all follow the same basic rule, can create a form of collective intelligence on the macroscopic level.
Colonies of social insects perfectly illustrate this model which greatly differs from human societies.
[12] They move through their surrounding area to carry out certain tasks and only possess a very limited amount of information to do so.
A colony of ants, for example, represents numerous qualities that can also be applied to a network of ambient objects.
Colonies of ants have a very high capacity to adapt themselves to changes in the environment, as well as great strength in dealing with situations where one individual fails to carry out a given task.
Parcels of information that move from a computer to a digital object behave in the same way as ants would do.
Pheromone is used by social insects such as bees, ants and termites; both for inter-agent and agent-swarm communications.
Pheromone-based communication was implemented by different means such as chemical [14][15][16] or physical (RFID tags,[17] light,[18][19][20][21] sound[22]) ways.
Using projected light was presented in a 2007 IEEE paper by Garnier, Simon, et al. as an experimental setup to study pheromone-based communication with micro autonomous robots.
In the first step of each iteration, each ant stochastically constructs a solution, i.e. the order in which the edges in the graph should be followed.
The elitist strategy has as its objective directing the search of all ants to construct a solution to contain links of the current best route.
To avoid stagnation of the search algorithm, the range of possible pheromone amounts on each trail is limited to an interval [τmax,τmin].
Seven communication methods for updating the pheromone level between groups in ACS are proposed and work on the traveling salesman problem.
[29] 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.
[30] It is a recursive form of ant system which divides the whole search domain into several sub-domains and solves the objective on these subdomains.
The subdomains corresponding to the selected results are further subdivided and the process is repeated until an output of desired precision is obtained.
[32] For some versions of the algorithm, it is possible to prove that it is convergent (i.e., it is able to find the global optimum in finite time).
The first ACO algorithm was called the ant system[26] and it was aimed to solve the travelling salesman problem, in which the goal is to find the shortest round-trip to link a series of cities.
The general algorithm is relatively simple and based on a set of ants, each making one of the possible round-trips along the cities.
As example can be considered antennas RFID-tags based on ant colony algorithms (ACO),[77] loopback and unloopback vibrators 10×10[76] The ACO algorithm is used in image processing for image edge detection and edge linking.
The movement of ants from one pixel to another is directed by the local variation of the image's intensity values.
Once the K ants have moved a fixed distance L for N iteration, the decision whether it is an edge or not is based on the threshold T on the pheromone matrix τ.
This principle has led some authors to create the term "value" to organize methods and behavior based on search of food, sorting larvae, division of labour and cooperative transportation.
