A Python3 implementation of the Ant Colony Optimization Meta-Heuristic
Installation is not possible at this time. Repository is under development.
Pants provides you with the ability to quickly determine how to visit a collection of interconnected nodes such that the work done is minimized. Nodes can be any arbitrary collection of data while the edges represent the amount of "work" required to travel between two nodes. Thus, Pants is a tool for solving traveling salesman problems.
The world is built from a list of nodes and a function responsible for returning the length of the edge between any two given nodes. The length function need not return actual length. Instead, "length" refers to that the amount of "work" involved in moving from the first node to the second node - whatever that "work" may be. For a silly, random example, it could even be the number of dishes one must wash before moving to the next station at a least dish-washing dish washer competition.
Solutions are found through an iterative process. In each iteration, several ants are allowed to find a solution that "visits" every node of the world. The amount of pheromone on each edge is updated according to the length of the solutions in which it was used. The ant that traveled the least distance is considered to be the local best solution. If the local solution has a shorter distance than the best from any previous iteration, it then becomes the global best solution. The elite ant(s) then deposit their pheromone along the path of the global best solution to strengthen it further, and the process repeats.
You can read more about Ant Colony Optimization on Wikipedia.
- Robert Grant [email protected] (Original author)
- Fabian Witt @witt_fabian (forked by)
GNU GENERAL PUBLIC LICENSE
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