Metadata-Version: 2.1
Name: aco-routing
Version: 1.0.6
Summary: A Python package to find the shortest path in a graph using Ant Colony Optimization (ACO)
Home-page: https://github.com/hasnainroopawalla/ant-colony-optimization
Author: Hasnain Roopawalla
Author-email: hasnain.roopawalla@gmail.com
License: MIT
Keywords: python,machinelearning,ai,aco
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown

Ant Colony Optimization

[![Develop](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/develop.yml/badge.svg)](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/develop.yml)
[![Deploy](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/deploy.yml/badge.svg)](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/actions/workflows/deploy.yml)
[![PyPi version](https://img.shields.io/pypi/v/aco_routing.svg)](https://pypi.python.org/pypi/aco_routing/)
[![Python versions](https://img.shields.io/pypi/pyversions/aco_routing.svg?style=plastic)](https://img.shields.io/pypi/pyversions/aco_routing.svg?style=plastic)
![Downloads](https://img.shields.io/pypi/dm/aco_routing.svg)

A Python package to find the shortest path in a graph using Ant Colony Optimization (ACO).

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)).

## 📝 Table of Contents

- [Getting Started](#getting_started)
- [Usage](#usage)
- [Contents](#contents)

## 🏁 Getting Started 

### To install the package directly from PyPi:
```
$ pip install aco_routing
```


## 🎈 Usage 
> **_Check out:_** [example.py](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/blob/00cd068597ab9a69a8eb81c8a3fd984797d2eefd/example.py)

Import all the dependencies.
```python
from aco_routing import Graph, Dijkstra, ACO
```

Create a `Graph` object.
```python
graph = Graph()
```

Create `Edges` between `Nodes` (nodes are implicitly created if they don't exist).
```python
graph.add_edge("A", "B", travel_time=2)
graph.add_edge("B", "C", travel_time=2)
graph.add_edge("A", "H", travel_time=2)
graph.add_edge("H", "G", travel_time=2)
graph.add_edge("C", "F", travel_time=1)
graph.add_edge("F", "G", travel_time=1)
graph.add_edge("G", "F", travel_time=1)
graph.add_edge("F", "C", travel_time=1)
graph.add_edge("C", "D", travel_time=10)
graph.add_edge("E", "D", travel_time=2)
graph.add_edge("G", "E", travel_time=2)
```

Define a `source` and `destination` as well as create objects for the `Dijkstra` and `ACO` classes.
```python
source = "A"
destination = "D"

aco = ACO(graph)
dijkstra = Dijkstra(graph)
```

Find the shortest path between the `source` and `destination` as well the cost of the path using `Dijkstra` and `ACO`.
```python
dijkstra_path, dijkstra_cost = dijkstra.find_shortest_path(source, destination)
aco_path, aco_cost = aco.find_shortest_path(source, destination)

print(f"ACO - path: {aco_path}, cost: {aco_cost}")
print(f"Dijkstra - path: {dijkstra_path}, cost: {dijkstra_cost}")
```

Output:
```
ACO - path: ['A', 'H', 'G', 'E', 'D'], cost: 8.0
Dijkstra - path: ['A', 'H', 'G', 'E', 'D'], cost: 8.0
```

## 📦 Contents 

### Graph
`aco_routing.Graph`
- A Directed Graph class which consists of `Nodes` and `Edges`.
- The `evaporation_rate` is initialized here.

### Node
`aco_routing.Node`
- A `Node` class which represents a node in the Graph and consists of various outgoing edges.

### Edge
`aco_routing.Edge`
- An `Edge` class which represents a link between 2 nodes in the Graph.
- Each `Edge` has 2 parameters:
    - `travel_time`: The amount of time it takes to traverse the edge. A high value indicates more traffic.
    - `pheromones`: A heuristic parameter i.e., the pheromone values deposited by the ants.

### Dijkstra
`aco_routing.Dijkstra`
- The baseline algorithm to compare the results of the candidate algorithm with.
- The Dijkstra's algorithm ([source](https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm)) returns the shortest path between any 2 nodes in a graph.

### Ant
`aco_routing.Ant`
- The `Ant` class representing an ant that traverses the graph.

### ACO
`aco_routing.ACO`
- The traditional Ant Colony Optimization algorithm that spawns various ants at random nodes and tries to find the shortest path between the specified source and destination.

## 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.

## License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/hasnainroopawalla/Ant-Colony-Optimization/blob/73b65a6fd14e3e5517b479cfecac1140f0ae7899/LICENSE) file for details.

