17-12-2014, 08:39 PM
ns2 code is needed for implementing sinkhole detection in wsn, using ant colony optimization
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ant colony optimization ns2 code
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ns2 code is needed for implementing sinkhole detection in wsn, using ant colony optimization
15-04-2017, 01:27 PM
In computation and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.
This algorithm is a member of the family of ant colony algorithms, in swarm intelligence methods, and constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his doctoral thesis, the first algorithm looked for an optimal path in a graph, based on the behavior of the ants that looked for a way between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have arisen, which are based on various aspects of ant behavior.
In the natural world, ants (initially) roam at random, and upon finding the return of food to their colony, while establishing the pheromone trails. If other ants find this way, they probably will not continue to travel randomly, but will follow the path, returning and reinforcing it if they finally find food.
Over time, however, the pheromone trail begins to evaporate, thus reducing its attractive strength. The longer it takes an ant to travel down the road and back again, the longer the pheromones have to evaporate. A short path, by comparison, is paraded more frequently, and therefore the density of pheromones becomes higher in trajectories shorter than the longer ones. Pheromone evaporation also has the advantage of avoiding convergence to a locally optimal solution. If there were no evaporation, the paths chosen by the first ants would tend to be excessively attractive to the next ones. In that case, the exploration of solution space would be limited.
Therefore, when an ant finds a good (ie short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads to all ants following a single path . The idea of the ant colony algorithm is to mimic this behavior with "simulated ants" by walking around the graph representing the problem to be solved.
This publication contains extensions to NS-2 (versions 2.27 and 2.28) that allows ACO behavior in WSN. The module is adaptable to the current version of NS-2 as it is installed as a new routing protocol. Written mainly in C ++, the ACO module (AntSense) produces the main attributes, however it was designed to be used by any other technology supported by this simulator.
This algorithm is a member of the family of ant colony algorithms, in swarm intelligence methods, and constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his doctoral thesis, the first algorithm looked for an optimal path in a graph, based on the behavior of the ants that looked for a way between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have arisen, which are based on various aspects of ant behavior.
In the natural world, ants (initially) roam at random, and upon finding the return of food to their colony, while establishing the pheromone trails. If other ants find this way, they probably will not continue to travel randomly, but will follow the path, returning and reinforcing it if they finally find food.
Over time, however, the pheromone trail begins to evaporate, thus reducing its attractive strength. The longer it takes an ant to travel down the road and back again, the longer the pheromones have to evaporate. A short path, by comparison, is paraded more frequently, and therefore the density of pheromones becomes higher in trajectories shorter than the longer ones. Pheromone evaporation also has the advantage of avoiding convergence to a locally optimal solution. If there were no evaporation, the paths chosen by the first ants would tend to be excessively attractive to the next ones. In that case, the exploration of solution space would be limited.
Therefore, when an ant finds a good (ie short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads to all ants following a single path . The idea of the ant colony algorithm is to mimic this behavior with "simulated ants" by walking around the graph representing the problem to be solved.
This publication contains extensions to NS-2 (versions 2.27 and 2.28) that allows ACO behavior in WSN. The module is adaptable to the current version of NS-2 as it is installed as a new routing protocol. Written mainly in C ++, the ACO module (AntSense) produces the main attributes, however it was designed to be used by any other technology supported by this simulator.
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