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# Copyright (c) 2022, Manfred Moitzi
# License: MIT License
# test data: http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsp/
import random
from typing import cast
from ezdxf.addons import genetic_algorithm as ga
from ezdxf.math import Vec2
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
bayg29 = [
(1150.0, 1760.0),
(630.0, 1660.0),
(40.0, 2090.0),
(750.0, 1100.0),
(750.0, 2030.0),
(1030.0, 2070.0),
(1650.0, 650.0),
(1490.0, 1630.0),
(790.0, 2260.0),
(710.0, 1310.0),
(840.0, 550.0),
(1170.0, 2300.0),
(970.0, 1340.0),
(510.0, 700.0),
(750.0, 900.0),
(1280.0, 1200.0),
(230.0, 590.0),
(460.0, 860.0),
(1040.0, 950.0),
(590.0, 1390.0),
(830.0, 1770.0),
(490.0, 500.0),
(1840.0, 1240.0),
(1260.0, 1500.0),
(1280.0, 790.0),
(490.0, 2130.0),
(1460.0, 1420.0),
(1260.0, 1910.0),
(360.0, 1980.0),
]
# RandomSwapMutate (swapping cities randomly) is very bad for this kind of
# problem! Changing the order of cities in a local environment is much better:
# - SwapNeighborMutate()
# - ReverseMutate()
# - ScrambleMutate()
# seed = 44
# DNA strands = 300 (population)
# elitism = 30
# selection = RankBasedSelection
# crossover_rate = 0.9, MateOrderedCX()
# mutate_rate = 0.07, ScrambleMutate(5)
BEST_OVERALL = -9074.150
ELITISM = 30
def sum_dist(points):
points.append(points[0])
return sum(p1.distance(p2) for p1, p2 in zip(points, points[1:]))
class TSPEvaluator(ga.Evaluator):
"""Traveling Salesmen Problem"""
def __init__(self, data):
self.cities = Vec2.list(data)
def evaluate(self, dna: ga.DNA) -> float:
# searching for shortest path
return -sum_dist([self.cities[i] for i in dna])
def show_log(log: ga.Log, name: str):
x = []
y = []
avg = []
for index, entry in enumerate(log.entries, start=1):
x.append(index)
y.append(abs(entry.fitness))
avg.append(abs(entry.avg_fitness))
fig, ax = plt.subplots()
ax.plot(x, y)
ax.plot(x, avg)
ax.set(xlabel="generation", ylabel="fitness", title=f"TSP: {name}")
ax.grid()
plt.show()
def show_result(data, order: ga.DNA, name):
x = []
y = []
for city in order:
x.append(data[city][0])
y.append(data[city][1])
fig, ax = plt.subplots()
ax.scatter(x, y)
x.append(x[0])
y.append(y[0])
ax.plot(x, y)
ax.set(title=f"TSP: {name}")
ax.grid()
plt.show()
def feedback(optimizer: ga.GeneticOptimizer):
print(
f"gen: {optimizer.generation:4}, "
f"stag: {optimizer.stagnation:4}, "
f"fitness: {abs(optimizer.best_fitness):.3f}"
)
return False
def genetic_probing(data, seed):
random.seed(seed)
optimizer = ga.GeneticOptimizer(
TSPEvaluator(data), 1000, max_fitness=BEST_OVERALL
)
optimizer.reset_fitness(-1e99) # required for searching for shortest path
optimizer.max_stagnation = 300
optimizer.selection = ga.RankBasedSelection()
optimizer.mate = ga.MateOrderedCX()
optimizer.crossover_rate = 0.9
optimizer.mutation = ga.ScrambleMutate(5)
optimizer.mutation_rate = 0.1
# count >= elitism, stores the <count> overall best solutions
optimizer.hall_of_fame.count = ELITISM
# preserve <elitism> overall best solutions in each generation
optimizer.elitism = ELITISM
optimizer.add_candidates(ga.UniqueIntDNA.n_random(300, length=len(data)))
optimizer.execute(feedback, 2)
print(
f"GeneticOptimizer: {optimizer.generation} generations x {optimizer.count} "
f"DNA strands, best result:"
)
evaluator = cast(TSPEvaluator, optimizer.evaluator)
best_dist = abs(evaluator.evaluate(optimizer.best_dna))
percent = best_dist / abs(BEST_OVERALL) * 100
print(f"Shortest path overall: {abs(BEST_OVERALL):.3f}")
print(
f"Shortest path found (seed={seed}): {best_dist:.3f} ({percent:.1f}%)"
)
print(optimizer.best_dna)
name = f"bay29, dist={int(best_dist)} ({percent:.1f}%), seed={seed}"
show_log(optimizer.log, name)
show_result(data, optimizer.best_dna, name)
if __name__ == "__main__":
for s in range(40, 50):
genetic_probing(bayg29, s)
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