/ACO

Ant Colony Optimization algorithm in Python

Primary LanguagePythonMIT LicenseMIT

Ant Colony Optimization

Implementation of the Ant Colony Optimization algorithm in Python

Currently works on 2D Cartesian coordinate system

Installation

From PyPi

pip install aco

Using Poetry

poetry add aco

Usage

AntColony(
    nodes,
    start=None,
    ant_count=300,
    alpha=0.5,
    beta=1.2,
    pheromone_evaporation_rate=0.40,
    pheromone_constant=1000.0,
    iterations=300,
)

Travelling Salesman Problem

import matplotlib.pyplot as plt
import random

from aco import AntColony


plt.style.use("dark_background")


COORDS = (
    (20, 52),
    (43, 50),
    (20, 84),
    (70, 65),
    (29, 90),
    (87, 83),
    (73, 23),
)


def random_coord():
    r = random.randint(0, len(COORDS))
    return r


def plot_nodes(w=12, h=8):
    for x, y in COORDS:
        plt.plot(x, y, "g.", markersize=15)
    plt.axis("off")
    fig = plt.gcf()
    fig.set_size_inches([w, h])


def plot_all_edges():
    paths = ((a, b) for a in COORDS for b in COORDS)

    for a, b in paths:
        plt.plot((a[0], b[0]), (a[1], b[1]))


plot_nodes()

colony = AntColony(COORDS, ant_count=300, iterations=300)

optimal_nodes = colony.get_path()

for i in range(len(optimal_nodes) - 1):
    plt.plot(
        (optimal_nodes[i][0], optimal_nodes[i + 1][0]),
        (optimal_nodes[i][1], optimal_nodes[i + 1][1]),
    )


plt.show()

screenshot

Reference