From 7b1f150daef8caea7edc2f0a7495b4f5a36b3402 Mon Sep 17 00:00:00 2001
From: Clarkzzzzz <clarkzzzzz@foxmail.com>
Date: Thu, 16 Nov 2023 15:58:14 +0800
Subject: [PATCH 1/5] add ant_colonyant_colony_optimization_algorithms.py
---
graphs/ant_colony_optimization_algorithms.py | 136 +++++++++++++++++++
1 file changed, 136 insertions(+)
create mode 100644 graphs/ant_colony_optimization_algorithms.py
diff --git a/graphs/ant_colony_optimization_algorithms.py b/graphs/ant_colony_optimization_algorithms.py
new file mode 100644
index 000000000000..ba14586921f0
--- /dev/null
+++ b/graphs/ant_colony_optimization_algorithms.py
@@ -0,0 +1,136 @@
+"""
+A simple example uses the ant colony optimization algorithm
+to solve the classic TSP problem
+https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms
+Author: Clark
+"""
+
+import copy
+import random
+
+
+def distance(city1: list[int], city2: list[int]) -> float:
+ """
+ Calculate the distance between two coordinate points
+ >>> distance([0,0],[3,4] )
+ 5.0
+ """
+ return (((city1[0] - city2[0]) ** 2) + ((city1[1] - city2[1]) ** 2)) ** 0.5
+
+
+def pheromone_update(
+ pheromone: list[list],
+ cities: dict[int, list[int]],
+ pheromone_evaporation: float,
+ ants_route: list[list[int]],
+ q: float,
+ best_path: list,
+ best_distance: float,
+) -> tuple[list[list], list, float]:
+ """
+ Update pheromones on the route and update the best route
+ """
+ for a in range(cities_num): # Update the volatilization of pheromone on all routes
+ for b in range(cities_num):
+ pheromone[a][b] *= pheromone_evaporation
+ for ant_route in ants_route:
+ total_distance = 0.0
+ for i in range(len(ant_route) - 1): # Calculate total distance
+ total_distance += distance(cities[ant_route[i]], cities[ant_route[i + 1]])
+ delta_pheromone = q / total_distance
+ for i in range(len(ant_route) - 1): # Update pheromones
+ pheromone[ant_route[i]][ant_route[i + 1]] += delta_pheromone
+ pheromone[ant_route[i + 1]][ant_route[i]] = pheromone[ant_route[i]][
+ ant_route[i + 1]
+ ]
+
+ if total_distance < best_distance:
+ best_path = ant_route
+ best_distance = total_distance
+
+ return pheromone, best_path, best_distance
+
+
+def city_select(
+ pheromone: list[list[int]],
+ 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
+ """
+ probabilities = []
+ for city in unvisited_cities:
+ city_distance = distance(
+ unvisited_cities[city], next(iter(current_city.values()))
+ )
+ probability = (pheromone[city][next(iter(current_city.keys()))] ** alpha) * (
+ (1 / city_distance) ** beta
+ )
+ probabilities.append(probability)
+
+ chosen_city_i = random.choices(
+ list(unvisited_cities.keys()), weights=probabilities
+ )[0]
+ chosen_city = {chosen_city_i: unvisited_cities[chosen_city_i]}
+ del unvisited_cities[next(iter(chosen_city.keys()))]
+ return chosen_city, unvisited_cities
+
+
+if __name__ == "__main__":
+ # City coordinates for TSP problem
+ cities = {
+ 0: [0, 0],
+ 1: [0, 5],
+ 2: [3, 8],
+ 3: [8, 10],
+ 4: [12, 8],
+ 5: [12, 4],
+ 6: [8, 0],
+ 7: [6, 2],
+ }
+
+ # Parameter settings
+ ANTS_NUM = 10 # Number of ants
+ ITERATIONS_NUM = 20 # Number of iterations
+ PHEROMONE_EVAPORATION = 0.7 # Pheromone volatilization coefficient,
+ # the larger the number, the greater the pheromone retention in each generation.
+ ALPHA = 1.0
+ BETA = 5.0
+ Q = 10.0 # Pheromone system parameters Q
+
+ # Initialize the pheromone matrix
+ cities_num = len(cities)
+ pheromone = [[1] * cities_num] * cities_num
+
+ best_path: list[int] = []
+ best_distance = float("inf")
+ for _ in range(ITERATIONS_NUM):
+ ants_route = []
+ for _ in range(ANTS_NUM):
+ unvisited_cities = copy.deepcopy(cities)
+ current_city = {next(iter(cities.keys())): next(iter(cities.values()))}
+ del unvisited_cities[next(iter(current_city.keys()))]
+ ant_route = [next(iter(current_city.keys()))]
+ while unvisited_cities:
+ current_city, unvisited_cities = city_select(
+ pheromone, current_city, unvisited_cities, ALPHA, BETA
+ )
+ ant_route.append(next(iter(current_city.keys())))
+ ant_route.append(0)
+ ants_route.append(ant_route)
+
+ pheromone, best_path, best_distance = pheromone_update(
+ pheromone,
+ cities,
+ PHEROMONE_EVAPORATION,
+ ants_route,
+ Q,
+ best_path,
+ best_distance,
+ )
+
+ print("Best Path:", best_path)
+ print("Best Distance:", best_distance)
From 38cae58c9c387394257f0b836986ee43a63b9f5c Mon Sep 17 00:00:00 2001
From: Clarkzzzzz <clarkzzzzz@foxmail.com>
Date: Fri, 17 Nov 2023 18:38:23 +0800
Subject: [PATCH 2/5] Modify details
---
graphs/ant_colony_optimization_algorithms.py | 134 +++++++++++++------
1 file changed, 93 insertions(+), 41 deletions(-)
diff --git a/graphs/ant_colony_optimization_algorithms.py b/graphs/ant_colony_optimization_algorithms.py
index ba14586921f0..4add378ee1d0 100644
--- a/graphs/ant_colony_optimization_algorithms.py
+++ b/graphs/ant_colony_optimization_algorithms.py
@@ -1,7 +1,14 @@
"""
A simple example uses the ant colony optimization algorithm
-to solve the classic TSP problem
+to solve the classic TSP problem.
+The travelling salesman problem (TSP) 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
"""
@@ -9,29 +16,94 @@
import random
+def main(
+ cities: dict[int, list[int]],
+ ants_num: int,
+ iterations_num: int,
+ pheromone_evaporation: float,
+ alpha: float,
+ beta: float,
+ q: float, # Pheromone system parameters Q，which is a constant
+) -> tuple[list[int], float]:
+ """
+ Ant colony algorithm main function
+ >>> 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
+ >>> main(cities,ANTS_NUM,ITERATIONS_NUM,PHEROMONE_EVAPORATION,ALPHA,BETA,Q)
+ ([0, 1, 0], 5.656854249492381)
+ """
+ # Initialize the pheromone matrix
+ cities_num = len(cities)
+ pheromone = [[1.0] * cities_num] * cities_num
+
+ best_path: list[int] = []
+ best_distance = float("inf")
+ for _ in range(iterations_num):
+ ants_route = []
+ for _ in range(ants_num):
+ unvisited_cities = copy.deepcopy(cities)
+ current_city = {next(iter(cities.keys())): next(iter(cities.values()))}
+ del unvisited_cities[next(iter(current_city.keys()))]
+ ant_route = [next(iter(current_city.keys()))]
+ while unvisited_cities:
+ current_city, unvisited_cities = city_select(
+ pheromone, current_city, unvisited_cities, alpha, beta
+ )
+ ant_route.append(next(iter(current_city.keys())))
+ ant_route.append(0)
+ ants_route.append(ant_route)
+
+ pheromone, best_path, best_distance = pheromone_update(
+ pheromone,
+ cities,
+ pheromone_evaporation,
+ ants_route,
+ q,
+ best_path,
+ best_distance,
+ )
+ return best_path, best_distance
+
+
def distance(city1: list[int], city2: list[int]) -> float:
"""
Calculate the distance between two coordinate points
- >>> distance([0,0],[3,4] )
+ >>> distance([0,0], [3,4] )
5.0
"""
return (((city1[0] - city2[0]) ** 2) + ((city1[1] - city2[1]) ** 2)) ** 0.5
def pheromone_update(
- pheromone: list[list],
+ pheromone: list[list[float]],
cities: dict[int, list[int]],
pheromone_evaporation: float,
ants_route: list[list[int]],
- q: float,
+ q: float, # Pheromone system parameters Q，which is a constant
best_path: list,
best_distance: float,
-) -> tuple[list[list], list, float]:
+) -> tuple[list[list[float]], list, float]:
"""
Update pheromones on the route and update the best route
+ >>> 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")
+ >>> pheromone_update(
+ ... pheromone,cities,PHEROMONE_EVAPORATION,ants_route,Q,best_path,best_distance
+ ... )
+ ([[0.7, 4.235533905932737], [4.235533905932737, 0.7]], [0, 1, 0], 5.656854249492381)
"""
- for a in range(cities_num): # Update the volatilization of pheromone on all routes
- for b in range(cities_num):
+ for a in range(len(cities)): # Update the volatilization of pheromone on all routes
+ for b in range(len(cities)):
pheromone[a][b] *= pheromone_evaporation
for ant_route in ants_route:
total_distance = 0.0
@@ -52,7 +124,7 @@ def pheromone_update(
def city_select(
- pheromone: list[list[int]],
+ pheromone: list[list[float]],
current_city: dict[int, list[int]],
unvisited_cities: dict[int, list[int]],
alpha: float,
@@ -60,6 +132,13 @@ def city_select(
) -> tuple[dict[int, list[int]], dict[int, list[int]]]:
"""
Choose the next city for ants
+ >>> 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
+ >>> city_select(pheromone,current_city,unvisited_cities,ALPHA,BETA)
+ ({1: [2, 2]}, {})
"""
probabilities = []
for city in unvisited_cities:
@@ -99,38 +178,11 @@ def city_select(
# the larger the number, the greater the pheromone retention in each generation.
ALPHA = 1.0
BETA = 5.0
- Q = 10.0 # Pheromone system parameters Q
-
- # Initialize the pheromone matrix
- cities_num = len(cities)
- pheromone = [[1] * cities_num] * cities_num
+ Q = 10.0 # Pheromone system parameters Q，which is a constant
- best_path: list[int] = []
- best_distance = float("inf")
- for _ in range(ITERATIONS_NUM):
- ants_route = []
- for _ in range(ANTS_NUM):
- unvisited_cities = copy.deepcopy(cities)
- current_city = {next(iter(cities.keys())): next(iter(cities.values()))}
- del unvisited_cities[next(iter(current_city.keys()))]
- ant_route = [next(iter(current_city.keys()))]
- while unvisited_cities:
- current_city, unvisited_cities = city_select(
- pheromone, current_city, unvisited_cities, ALPHA, BETA
- )
- ant_route.append(next(iter(current_city.keys())))
- ant_route.append(0)
- ants_route.append(ant_route)
-
- pheromone, best_path, best_distance = pheromone_update(
- pheromone,
- cities,
- PHEROMONE_EVAPORATION,
- ants_route,
- Q,
- best_path,
- best_distance,
- )
+ best_path, best_distance = main(
+ cities, ANTS_NUM, ITERATIONS_NUM, PHEROMONE_EVAPORATION, ALPHA, BETA, Q
+ )
- print("Best Path:", best_path)
- print("Best Distance:", best_distance)
+ print(f"{best_path = }")
+ print(f"{best_distance = }")
From 6a8e37d92cdf4d81cb5d3670b992955264567b40 Mon Sep 17 00:00:00 2001
From: Clarkzzzzz <clarkzzzzz@foxmail.com>
Date: Fri, 17 Nov 2023 18:41:54 +0800
Subject: [PATCH 3/5] Modify type annotation
---
graphs/ant_colony_optimization_algorithms.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/graphs/ant_colony_optimization_algorithms.py b/graphs/ant_colony_optimization_algorithms.py
index 4add378ee1d0..8de2145821a4 100644
--- a/graphs/ant_colony_optimization_algorithms.py
+++ b/graphs/ant_colony_optimization_algorithms.py
@@ -85,9 +85,9 @@ def pheromone_update(
pheromone_evaporation: float,
ants_route: list[list[int]],
q: float, # Pheromone system parameters Q，which is a constant
- best_path: list,
+ best_path: list[int],
best_distance: float,
-) -> tuple[list[list[float]], list, float]:
+) -> tuple[list[list[float]], list[int], float]:
"""
Update pheromones on the route and update the best route
>>> pheromone = [[1.0,1.0],[1.0,1.0]]
From ece71ff91128fecb383f6ca9e39422cb29e8e3a1 Mon Sep 17 00:00:00 2001
From: Christian Clauss <cclauss@me.com>
Date: Sat, 25 Nov 2023 13:18:30 +0100
Subject: [PATCH 4/5] Add tests for KeyError, IndexError, StopIteration, etc.
---
graphs/ant_colony_optimization_algorithms.py | 140 +++++++++++--------
1 file changed, 85 insertions(+), 55 deletions(-)
diff --git a/graphs/ant_colony_optimization_algorithms.py b/graphs/ant_colony_optimization_algorithms.py
index 8de2145821a4..5257d6b64879 100644
--- a/graphs/ant_colony_optimization_algorithms.py
+++ b/graphs/ant_colony_optimization_algorithms.py
@@ -1,10 +1,9 @@
"""
-A simple example uses the ant colony optimization algorithm
-to solve the classic TSP problem.
-The travelling salesman problem (TSP) 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?"
+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
@@ -15,6 +14,16 @@
import copy
import random
+cities = {
+ 0: [0, 0],
+ 1: [0, 5],
+ 2: [3, 8],
+ 3: [8, 10],
+ 4: [12, 8],
+ 5: [12, 4],
+ 6: [8, 0],
+ 7: [6, 2],
+}
def main(
cities: dict[int, list[int]],
@@ -27,14 +36,33 @@ def main(
) -> tuple[list[int], float]:
"""
Ant colony algorithm main function
- >>> 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
- >>> main(cities,ANTS_NUM,ITERATIONS_NUM,PHEROMONE_EVAPORATION,ALPHA,BETA,Q)
+ >>> 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):
+ ...
+ 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):
+ ...
+ 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)
"""
# Initialize the pheromone matrix
@@ -73,7 +101,11 @@ def main(
def distance(city1: list[int], city2: list[int]) -> float:
"""
Calculate the distance between two coordinate points
- >>> distance([0,0], [3,4] )
+ >>> distance([0, 0], [3, 4] )
+ 5.0
+ >>> distance([0, 0], [-3, 4] )
+ 5.0
+ >>> distance([0, 0], [-3, -4] )
5.0
"""
return (((city1[0] - city2[0]) ** 2) + ((city1[1] - city2[1]) ** 2)) ** 0.5
@@ -90,17 +122,26 @@ def pheromone_update(
) -> tuple[list[list[float]], list[int], float]:
"""
Update pheromones on the route and update the best route
- >>> 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")
- >>> pheromone_update(
- ... pheromone,cities,PHEROMONE_EVAPORATION,ants_route,Q,best_path,best_distance
- ... )
+ >>>
+ >>> 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):
+ ...
+ 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):
+ ...
+ KeyError: 0
"""
for a in range(len(cities)): # Update the volatilization of pheromone on all routes
for b in range(len(cities)):
@@ -132,13 +173,24 @@ def city_select(
) -> tuple[dict[int, list[int]], dict[int, list[int]]]:
"""
Choose the next city for ants
- >>> 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
- >>> city_select(pheromone,current_city,unvisited_cities,ALPHA,BETA)
+ >>> 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
"""
probabilities = []
for city in unvisited_cities:
@@ -159,30 +211,8 @@ def city_select(
if __name__ == "__main__":
- # City coordinates for TSP problem
- cities = {
- 0: [0, 0],
- 1: [0, 5],
- 2: [3, 8],
- 3: [8, 10],
- 4: [12, 8],
- 5: [12, 4],
- 6: [8, 0],
- 7: [6, 2],
- }
-
- # Parameter settings
- ANTS_NUM = 10 # Number of ants
- ITERATIONS_NUM = 20 # Number of iterations
- PHEROMONE_EVAPORATION = 0.7 # Pheromone volatilization coefficient,
- # the larger the number, the greater the pheromone retention in each generation.
- ALPHA = 1.0
- BETA = 5.0
- Q = 10.0 # Pheromone system parameters Q，which is a constant
-
- best_path, best_distance = main(
- cities, ANTS_NUM, ITERATIONS_NUM, PHEROMONE_EVAPORATION, ALPHA, BETA, Q
- )
+ best_path, best_distance = main(cities=cities, ants_num=10, iterations_num=20,
+ pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
print(f"{best_path = }")
print(f"{best_distance = }")
From 536d1664d3040e16032d61c957daa5faf1a24437 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
<66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sat, 25 Nov 2023 12:19:05 +0000
Subject: [PATCH 5/5] [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---
graphs/ant_colony_optimization_algorithms.py | 30 +++++++++++++-------
1 file changed, 19 insertions(+), 11 deletions(-)
diff --git a/graphs/ant_colony_optimization_algorithms.py b/graphs/ant_colony_optimization_algorithms.py
index 5257d6b64879..652ad6144297 100644
--- a/graphs/ant_colony_optimization_algorithms.py
+++ b/graphs/ant_colony_optimization_algorithms.py
@@ -15,16 +15,17 @@
import random
cities = {
- 0: [0, 0],
- 1: [0, 5],
- 2: [3, 8],
- 3: [8, 10],
- 4: [12, 8],
- 5: [12, 4],
- 6: [8, 0],
- 7: [6, 2],
+ 0: [0, 0],
+ 1: [0, 5],
+ 2: [3, 8],
+ 3: [8, 10],
+ 4: [12, 8],
+ 5: [12, 4],
+ 6: [8, 0],
+ 7: [6, 2],
}
+
def main(
cities: dict[int, list[int]],
ants_num: int,
@@ -122,7 +123,7 @@ def pheromone_update(
) -> 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=[],
@@ -211,8 +212,15 @@ def city_select(
if __name__ == "__main__":
- best_path, best_distance = main(cities=cities, ants_num=10, iterations_num=20,
- pheromone_evaporation=0.7, alpha=1.0, beta=5.0, q=10)
+ best_path, best_distance = main(
+ cities=cities,
+ ants_num=10,
+ iterations_num=20,
+ pheromone_evaporation=0.7,
+ alpha=1.0,
+ beta=5.0,
+ q=10,
+ )
print(f"{best_path = }")
print(f"{best_distance = }")