<h1 align="center">Ant Colony Optimization</h1>
[](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/develop.yml)
[](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/deploy.yml)
[](https://pypi.python.org/pypi/aco_routing/)
<!-- [](https://img.shields.io/pypi/pyversions/aco_routing.svg?style=plastic) -->
A Python package to find the shortest path in a graph using Ant Colony Optimization (ACO).
➡️ Check out my [Medium article](https://medium.com/@hasnain.roopawalla/ant-colony-optimization-1bbc346c2da5) for a detailed walkthrough 🚀
The Ant colony Optimization algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs ([source](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms)).
This implementation of the ACO algorithm uses the [NetworkX](https://networkx.org/) graph environment.
## 🏁 Getting Started <a name = "getting_started"></a>
### To install the package directly from PyPi:
```
$ pip install aco_routing
```
## 🎈 Usage <a name="usage"></a>
> Check out: [example.py](https://github.com/hasnainroopawalla/ant-colony-optimization/blob/master/example.py)
Import all the dependencies:
```python
from aco_routing import ACO
import networkx as nx
```
Create a `NetworkX.Graph` object with nodes and edges:
```python
G = nx.DiGraph()
G.add_edge("A", "B", cost=2)
G.add_edge("B", "C", cost=2)
G.add_edge("A", "H", cost=2)
G.add_edge("H", "G", cost=2)
G.add_edge("C", "F", cost=1)
G.add_edge("F", "G", cost=1)
G.add_edge("G", "F", cost=1)
G.add_edge("F", "C", cost=1)
G.add_edge("C", "D", cost=10)
G.add_edge("E", "D", cost=2)
G.add_edge("G", "E", cost=2)
```
Use ACO to find the shortest path and cost between the `source` and `destination`:
```python
aco = ACO(G, ant_max_steps=100, num_iterations=100, ant_random_spawn=True)
aco_path, aco_cost = aco.find_shortest_path(
source="A",
destination="D",
num_ants=100,
)
```
Output:
```
ACO path: A -> H -> G -> E -> D
ACO path cost: 8.0
```
## 📦 Contents <a name = "contents"></a>
### Ant
`aco_routing.Ant`
- An `Ant` that traverses the graph.
### ACO
`aco_routing.ACO`
- The traditional Ant Colony Optimization algorithm that spawns ants at various nodes in the graph and finds the shortest path between the specified source and destination ([pseudo code](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms#Algorithm_and_formula)).
## Contributing
- Post any issues and suggestions on the GitHub [issues](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/issues) page.
- To contribute, fork the project and then create a pull request back to master.
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"description": "<h1 align=\"center\">Ant Colony Optimization</h1>\n\n[](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/develop.yml)\n[](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/deploy.yml)\n[](https://pypi.python.org/pypi/aco_routing/)\n\n\n<!-- [](https://img.shields.io/pypi/pyversions/aco_routing.svg?style=plastic) -->\n\nA Python package to find the shortest path in a graph using Ant Colony Optimization (ACO).\n\n\u27a1\ufe0f Check out my [Medium article](https://medium.com/@hasnain.roopawalla/ant-colony-optimization-1bbc346c2da5) for a detailed walkthrough \ud83d\ude80\n\nThe Ant colony Optimization algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs ([source](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms)).\n\nThis implementation of the ACO algorithm uses the [NetworkX](https://networkx.org/) graph environment.\n\n## \ud83c\udfc1 Getting Started <a name = \"getting_started\"></a>\n\n### To install the package directly from PyPi:\n\n```\n$ pip install aco_routing\n```\n\n## \ud83c\udf88 Usage <a name=\"usage\"></a>\n\n> Check out: [example.py](https://github.com/hasnainroopawalla/ant-colony-optimization/blob/master/example.py)\n\nImport all the dependencies:\n\n```python\nfrom aco_routing import ACO\nimport networkx as nx\n```\n\nCreate a `NetworkX.Graph` object with nodes and edges:\n\n```python\nG = nx.DiGraph()\n\nG.add_edge(\"A\", \"B\", cost=2)\nG.add_edge(\"B\", \"C\", cost=2)\nG.add_edge(\"A\", \"H\", cost=2)\nG.add_edge(\"H\", \"G\", cost=2)\nG.add_edge(\"C\", \"F\", cost=1)\nG.add_edge(\"F\", \"G\", cost=1)\nG.add_edge(\"G\", \"F\", cost=1)\nG.add_edge(\"F\", \"C\", cost=1)\nG.add_edge(\"C\", \"D\", cost=10)\nG.add_edge(\"E\", \"D\", cost=2)\nG.add_edge(\"G\", \"E\", cost=2)\n```\n\nUse ACO to find the shortest path and cost between the `source` and `destination`:\n\n```python\naco = ACO(G, ant_max_steps=100, num_iterations=100, ant_random_spawn=True)\n\naco_path, aco_cost = aco.find_shortest_path(\n source=\"A\",\n destination=\"D\",\n num_ants=100,\n)\n```\n\nOutput:\n\n```\nACO path: A -> H -> G -> E -> D\nACO path cost: 8.0\n```\n\n## \ud83d\udce6 Contents <a name = \"contents\"></a>\n\n### Ant\n\n`aco_routing.Ant`\n\n- An `Ant` that traverses the graph.\n\n### ACO\n\n`aco_routing.ACO`\n\n- The traditional Ant Colony Optimization algorithm that spawns ants at various nodes in the graph and finds the shortest path between the specified source and destination ([pseudo code](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms#Algorithm_and_formula)).\n\n## Contributing\n\n- Post any issues and suggestions on the GitHub [issues](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/issues) page.\n- To contribute, fork the project and then create a pull request back to master.\n\n\n",
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