def get_fitnessSoCPriority(guess_instance, apadCoordinates):
"""
Calculating fitness function for set of genes given with guess_instance.
Fitness function for now is just total distance between
:param guess_instance: instance of Guess class
:return: float (fitness)
"""
return 1 / (
1 + salesman.total_distance(guess_instance.points, apadCoordinates))
def scenarioGoToNearest():
system = parser(filename="evolutionary_init.txt")
mussel_coordinates = [list(item.coordinates) for item in system.mussels]
start = min(mussel_coordinates,
key=lambda x: distance(list(system.pads[0].coordinates), x))
points = go_to_nearest(system, start)
totalDistance = total_distance(points)
system.plot_topology(
annotate_energy=False,
order_of_passing=True,
title=
"Way of prioritizing: Go to nearest point with nearest first point; Total Distance: {}"
.format(totalDistance))
def main(points, apadCoordinates):
n = 25 # POPULATION SIZE
mut_prob = 0.08 # MUTATION PROBABILITY
number_of_iterations = 12500 # NUMBER OF ITERATION7
global points_dict
for i in range(0, len(points)):
points_dict[i + 1] = points[i]
best = k_gen_algorithm(points, n, number_of_iterations, mut_prob,
apadCoordinates)
print("Best guess is: {}".format([points_dict[item]
for item in best.gene]))
print("Best guess is: {}".format(best.gene))
print("Best length is: {}".format(
salesman.total_distance(best.points, apadCoordinates)))
print("Best fitness is: {}".format(get_fitness(best, apadCoordinates)))
return (best, points_dict)
def scenarioEvolutionary():
system = parser(filename="evolutionary_init.txt")
mussel_coordinates = [list(item.coordinates) for item in system.mussels]
bestGene, points_dict = evolutionary.main(mussel_coordinates,
system.pads[0].coordinates)
switchDict = dict()
for key, item in points_dict.items():
switchDict[tuple(item)] = key
# for i, mussel in enumerate(system.mussels):
points = bestGene.points
for i, mussel in enumerate(system.mussels):
system.mussels[i].order_of_passing = points.index(
list(mussel.coordinates))
totalDistance = total_distance(points, system.pads[0].coordinates)
system.plot_topology(
annotate_energy=False,
order_of_passing=True,
title="Way of prioritizing: Evolutionary algorithm; Total Distance: {}"
.format(totalDistance))