Ant Colony Optimization Analysis

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Introduction Ant Colony Optimization (ACO) [10, 11] technique is used to solve an optimization problem. For combinatorial optimization problem this has been developed. The artificial systems are studied by the Ant Colony Optimization, the behavior of real ants are inspiration for them. The ant colony algorithm is composed by a set of co-operating agents called ants. These ants co-operate with each other to find the good solutions to combinatorial optimization problems. The combinatorial optimization problems like maximum loadability in voltage control study, loss minimization in distribution networks, unit commitment problem, multiobjective reactive power compensation and complex multistage decision problem can be solved. N…show more content…
Between the food source F and the nest N, there is a path, through which ants are walking. This is the naturally observedHere all is well in the world of ants. All of a sudden an obstacle will block the path. At this situation all the ants which are walking from the nest to the food source and vice versa have to decide whether to turn right or left. Here the BCD is shorter than the path BHD. Therefore the ants which are following the path BCD will reach faster than the other path. The ants use the pheromone trails as a media to communicate among themselves. Here as the path BCD is shorter compared to the path BHD, the ants will reach faster and the more number of trails will be found in span of time. As a result the quantity of pheromone trails increases in the shorter path. This increases the probability with which the ants chooses a path increases in the preceding steps. Finally the shorter path is reinforced, all the ants will follow the shorter path. This can be quoted as an example for self-organized

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