ANT COLONY OPTIMIZATION
Ant Colony Optimization (ACO) technique is newly invented optimization technique to solve graphical optimization problem. It has been developed for combinatorial optimization problems. ACO are multi-agent system in which the behaviour of each single agent, called artificial ant or ant for short in the following, is inspired by the behaviour of real ants . ACO has been successfully employed to combinatorial optimization problems such as maximum loadability in voltage control study, loss minimization in distribution networks, unit commitment problem, multi0bjective reactive power compensation, and complex multi-stage decision problem.
ACO CONCEPT:
There is a path, along which ants are walking (for example from food source A to the nest E, and vice versa, as shown in Fig 1.
Suddenly an obstacle appears and the path is cut off. So at position B the ants walking from A to E (or at position D those walking in the opposite direction) have to decide whether to turn right or left. The path of ants followed is shown in Fig 2.
Because path BCD is shorter than BHD, the first ant following it will reach D before the first ant following path BHD which is shown in Fig.3. The result is that an ant returning from E to D will find a stronger trail on path DCB, caused by the half of all the ants that by chance decided to approach the obstacle via DCBA and by the already arrived ones coming via BCD: they will therefore prefer (in probability) path DCB to path DHB. As a consequence, the number of ants following path BCD per unit of time will be higher than the number of ants following EHD. This causes the quantity of pheromone on the shorter path to grow faster than on the longer one, and therefore the probability with which any single ant chooses the path to follow is quickly biased toward the shorter one. The final result is that very quickly all ants will choose the shorter path.
The algorithm that is going to be defined in the next sections is models derived from the study of real ant colonies. Therefore the system is called as Ant System (AS) and the algorithms as Ant algorithms.
The use of artificial ant colonies as an optimization tool, will have some major differences with a real (natural ant) one:
1. artificial ants will have some memory,
2. they will not be completely blind,
3. they will live in an environment where time is discrete.
Ant colony optimization is a major breakthrough in Engineering as well as non engineering applications
There is a path, along which ants are walking (for example from food source A to the nest E, and vice versa, as shown in Fig 1.
Suddenly an obstacle appears and the path is cut off. So at position B the ants walking from A to E (or at position D those walking in the opposite direction) have to decide whether to turn right or left. The path of ants followed is shown in Fig 2.
Because path BCD is shorter than BHD, the first ant following it will reach D before the first ant following path BHD which is shown in Fig.3. The result is that an ant returning from E to D will find a stronger trail on path DCB, caused by the half of all the ants that by chance decided to approach the obstacle via DCBA and by the already arrived ones coming via BCD: they will therefore prefer (in probability) path DCB to path DHB. As a consequence, the number of ants following path BCD per unit of time will be higher than the number of ants following EHD. This causes the quantity of pheromone on the shorter path to grow faster than on the longer one, and therefore the probability with which any single ant chooses the path to follow is quickly biased toward the shorter one. The final result is that very quickly all ants will choose the shorter path.
The algorithm that is going to be defined in the next sections is models derived from the study of real ant colonies. Therefore the system is called as Ant System (AS) and the algorithms as Ant algorithms.
The use of artificial ant colonies as an optimization tool, will have some major differences with a real (natural ant) one:
1. artificial ants will have some memory,
2. they will not be completely blind,
3. they will live in an environment where time is discrete.
Ant colony optimization is a major breakthrough in Engineering as well as non engineering applications
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