graphs.ant_colony_optimization_algorithms

Use an ant colony optimization algorithm to solve the travelling salesman problem (TSP) which asks the following question: “Given a list of cities and the distances between each pair of cities, what is the

shortest possible route that visits each city exactly once and returns to the origin city?”

https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms https://en.wikipedia.org/wiki/Travelling_salesman_problem

Author: Clark

Attributes

cities

Functions

city_select(→ tuple[dict[int, list[int]], dict[int, ...)	Choose the next city for ants
distance(→ float)	Calculate the distance between two coordinate points
main(→ tuple[list[int], float])	Ant colony algorithm main function
pheromone_update(→ tuple[list[list[float]], list[int], ...)	Update pheromones on the route and update the best route

Module Contents

graphs.ant_colony_optimization_algorithms.city_select(pheromone: list[list[float]], current_city: dict[int, list[int]], unvisited_cities: dict[int, list[int]], alpha: float, beta: float) → tuple[dict[int, list[int]], dict[int, list[int]]]

Choose the next city for ants >>> city_select(pheromone=[[1.0, 1.0], [1.0, 1.0]], current_city={0: [0, 0]}, … unvisited_cities={1: [2, 2]}, alpha=1.0, beta=5.0) ({1: [2, 2]}, {}) >>> city_select(pheromone=[], current_city={0: [0,0]}, … unvisited_cities={1: [2, 2]}, alpha=1.0, beta=5.0) Traceback (most recent call last):

…

IndexError: list index out of range >>> city_select(pheromone=[[1.0, 1.0], [1.0, 1.0]], current_city={}, … unvisited_cities={1: [2, 2]}, alpha=1.0, beta=5.0) Traceback (most recent call last):

…

StopIteration >>> city_select(pheromone=[[1.0, 1.0], [1.0, 1.0]], current_city={0: [0, 0]}, … unvisited_cities={}, alpha=1.0, beta=5.0) Traceback (most recent call last):

…

IndexError: list index out of range

graphs.ant_colony_optimization_algorithms.distance(city1: list[int], city2: list[int]) → float

Calculate the distance between two coordinate points >>> distance([0, 0], [3, 4] ) 5.0 >>> distance([0, 0], [-3, 4] ) 5.0 >>> distance([0, 0], [-3, -4] ) 5.0

graphs.ant_colony_optimization_algorithms.main(cities: dict[int, list[int]], ants_num: int, iterations_num: int, pheromone_evaporation: float, alpha: float, beta: float, q: float) → tuple[list[int], float]

Ant colony algorithm main function >>> main(cities=cities, ants_num=10, iterations_num=20, … pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10) ([0, 1, 2, 3, 4, 5, 6, 7, 0], 37.909778143828696) >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=5, … pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10) ([0, 1, 0], 5.656854249492381) >>> main(cities={0: [0, 0], 1: [2, 2], 4: [4, 4]}, ants_num=5, iterations_num=5, … pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10) Traceback (most recent call last):

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IndexError: list index out of range >>> main(cities={}, ants_num=5, iterations_num=5, … pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10) Traceback (most recent call last):

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StopIteration >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=0, iterations_num=5, … pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10) ([], inf) >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=0, … pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10) ([], inf) >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=5, … pheromone_evaporation=1, alpha=1.0, beta=5.0, q=10) ([0, 1, 0], 5.656854249492381) >>> main(cities={0: [0, 0], 1: [2, 2]}, ants_num=5, iterations_num=5, … pheromone_evaporation=0, alpha=1.0, beta=5.0, q=10) ([0, 1, 0], 5.656854249492381)

graphs.ant_colony_optimization_algorithms.pheromone_update(pheromone: list[list[float]], cities: dict[int, list[int]], pheromone_evaporation: float, ants_route: list[list[int]], q: float, best_path: list[int], best_distance: float) → tuple[list[list[float]], list[int], float]

Update pheromones on the route and update the best route >>> >>> pheromone_update(pheromone=[[1.0, 1.0], [1.0, 1.0]], … cities={0: [0,0], 1: [2,2]}, pheromone_evaporation=0.7, … ants_route=[[0, 1, 0]], q=10, best_path=[], … best_distance=float(“inf”)) ([[0.7, 4.235533905932737], [4.235533905932737, 0.7]], [0, 1, 0], 5.656854249492381) >>> pheromone_update(pheromone=[], … cities={0: [0,0], 1: [2,2]}, pheromone_evaporation=0.7, … ants_route=[[0, 1, 0]], q=10, best_path=[], … best_distance=float(“inf”)) Traceback (most recent call last):

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IndexError: list index out of range >>> pheromone_update(pheromone=[[1.0, 1.0], [1.0, 1.0]], … cities={}, pheromone_evaporation=0.7, … ants_route=[[0, 1, 0]], q=10, best_path=[], … best_distance=float(“inf”)) Traceback (most recent call last):

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KeyError: 0

graphs.ant_colony_optimization_algorithms.cities