To browse Academia. Edu and the wider internet faster and more securely, please take a few seconds to. Sorry, preview is currently unavailable. You can download the paper by clicking the button above. An ant colony optimization framework has been compared and shown to be a viable alternative approach to other stochastic search algorithms. The algorithm has been tested for variety of different benchmark test functions involving constrained and unconstrained NLP, MILP, and MINLP optimization problems.
Ant colony optimization techniques for the vehicle routing
The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. In the natural world, ants (initially) wander , and upon finding food return to their colony while laying down trails. If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food (see ). Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over more frequently, and thus the pheromone density becomes higher on shorter paths than longer ones.
Ant Colony Optimization Introduction and Recent Trends
Pheromone evaporation also has the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the paths chosen by the first ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution space would be constrained. Thus, when one ant finds a good (i. E. , short) path from the colony to a food source, other ants are more likely to follow that path, and eventually leads all the ants following a single path.
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