🐜 Initial commit

Author: UltimateDoge5

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README.md

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+# 🐜 ACO-TS
+Ant colony optimization algorithm wrote in typescript. You can see the demo in action [here](https://ultimatedoge5.github.io/ACO-TS/).
+
+## About
+ACO (Ant colony optimization) algorithm was invented to search for the (probabilistic) optimal path on a weighted graph and to solve the [travelling salesman problem](https://en.wikipedia.org/wiki/Travelling_salesman_problem).
+
+## Algorithm
+There are multiple [extensions](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms#Common_extensions) of the ant system, but I decided to implement the first and original one. This algorithm is heuristic which means that it will most likely give a better solution with more iterations.
+
+### Control variables
+In the ACO  algorithm, we need five variables to control the behaviour of our ants:
+* Alpha - states how much impact the pheromones have on the final score of an edge
+* Beta - determines how much influence the distance has on the final score of an edge
+* Pher - initial value of pheromones
+* Pho - defines the rate at which pheromones will evaporate
+* Q - indicates the number of pheromones
+
+Using other values than the default will change the behaviour of ants and their ability to determine paths.
+
+## Sources
+[Ant colony optimization](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms)