def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 2000
mating_pool_size = 300
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 100
advanced_Canada = {
"description": {
"instance_name": "none",
"algorithm": "none",
"city_all": 4663,
"generation_all": gen_limit
},
"evolutions": [
# nothing yet
]
}
advanced_Uruguay = {
"description": {
"instance_name": "none",
"algorithm": "none",
"city_all": 734,
"generation_all": gen_limit
},
"evolutions": [
# nothing yet
]
}
advanced_WesternSahara = {
"description": {
"instance_name": "none",
"algorithm": "none",
"city_all": 29,
"evolution_all": 10,
"generation_all": gen_limit,
"generation_size": mating_pool_size
},
"evolutions": [
# nothing yet
]
}
print(json.dumps(advanced_WesternSahara))
for evo in range(10): # Experiment
# construct a trial #
trial = [[{"time_cost": 0}]]
print("trial", evo + 1)
# t = time.time()
print("Preparing for the information...")
c = CityMap.CityMap(western29)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
init = Initialization.Population(popsize, city_map)
init.evalPopulation()
print("Initialization end.")
# Evolution ########################################################################################################
while gen < gen_limit:
# parent selection ---------------------------------------------------------------------------------------------
#print("parent selection...")
parents = Selection.tournament_selection(init.population[1:],
mating_pool_size,
tournament_size)
#print("parent selection end.")
offsprings = []
i = 0
while len(offsprings
) < mating_pool_size: # 2 parents -> 2 individuals
p1 = parents[i]
p2 = parents[i + 1]
# crossover ------------------------------------------------------------------------------------------------
#print("crossover...")
if random.random() < xover_rate:
#off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1 = Crossover.order_crossover(p1, p2, city_map)
off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
# mutation -------------------------------------------------------------------------------------------------
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
#off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
#off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
#off2 = Mutation.inversion_mutation(p2, city_map)
#print("Mutation end")
offsprings.append(off1)
offsprings.append(off2)
#print(len(offsprings))
i += 2
# survial selection --------------------------------------------------------------------------------------------
#print("survival selection")
init.population[1:] = Selection.mu_plus_lambda(
init.population[1:], offsprings)
#print("survival selection end")
init.evalPopulation()
print("generation:", gen, " Average length:", init.AverageLength,
" Longest length: ", init.worstTour.length,
" shortest length:", init.bestTour.length)
# construct a generation #
generation = {
"best-individual-sequence":
init.bestTour.tour,
"best-individual-distance":
init.bestTour.length,
"worst-individual-sequence":
init.worstTour.tour,
"worst-individual-distance":
init.worstTour.length,
"average-distance":
init.AverageLength,
"individual-distances":
[round(distance.length) for distance in init.population
] # distance.length for distance in init.population
}
trial.append(generation)
gen += 1
advanced_WesternSahara["evolutions"].append(trial)
print("time cost:", time.time())
# here processing generating json file #############################################################################
output_file = open("advanced_WesternSahara.js", "w")
output_file.write("let advanced_WesternSahara = ")
output_file.write(json.dumps(advanced_WesternSahara))
output_file.close()
def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 10000
mating_pool_size = 8000
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 10000
print("Preparing for the information...")
c = CityMap.CityMap(uruguay734)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
init = Initialization.Population(popsize, city_map)
init.evalPopulation()
print("Initialization end.")
#EA algorithm
while gen < gen_limit:
#parent selection
#print("parent selection...")
parents = Selection.tournament_selection(init.population[1:],
mating_pool_size,
tournament_size)
#parents = Selection.random_selection(init.population[1:], mating_pool_size)
#parents = Selection.MPS(init.population[1:], mating_pool_size)
offsprings = []
i = 0
while len(
offsprings) < mating_pool_size: # 2 parents -> 2 individuals
p1 = parents[i]
p2 = parents[i + 1]
# crossover ################################################################################################
#print("crossover...")
if random.random() < xover_rate:
#off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1, off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
# mutation #################################################################################################
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
#off1 = Mutation.WGWRGM(p1, city_map)
#off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
#off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#off2 = Mutation.WGWRGM(p2, city_map)
#off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
#off2 = Mutation.inversion_mutation(p2, city_map)
#print("Mutation end")
offsprings.append(off1)
offsprings.append(off2)
#print(len(offsprings))
i += 2
# survial selection ############################################################################################
#print("survival selection")
init.population = Selection.mu_plus_lambda(init.population, offsprings)
init.evalPopulation()
print("generation:", gen, " Average length:", init.AverageLength,
" Longest length: ", init.worstTour.length, " shortest length:",
init.bestTour.length)
gen += 1
[{"time_cost": 0}]
]
print("trial", evo+1)
print("Preparing for the information...")
c = CityMap.CityMap(western29)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
init = Initialization.Population(popsize, city_map)
=======
popsize = 500
mating_pool_size = 100
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 500
print("Preparing for the information...")
c = CityMap.CityMap(uruguay734)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 1000
mating_pool_size = 800
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 100
# choose the instance you are using ################################################################################
instance_name = "WesternSahara"
evolution_all = 10
source = None
city_all = 0
if instance_name == "WesternSahara":
city_all = 29
source = western29
if instance_name == "Uruguay":
city_all = 734
source = uruguay734
if instance_name == "Canada":
city_all = 4663
source = canada4663
experiment = {
"description": {
"instance_name": instance_name,
"algorithm": "advanced",
"city_all": city_all,
"evolution_all": evolution_all,
"generation_all": gen_limit,
"generation_size": popsize
},
"evolutions": [
# nothing yet
]
}
print(json.dumps(experiment))
for evo in range(evolution_all): # Experiment
# construct a trial #
trial = [{"time_cost": 0}]
print("trial", evo + 1)
start_time = time.time()
print("Preparing for the information...")
c = CityMap.CityMap(source)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
init = Initialization.Population(popsize, city_map)
init.evalPopulation()
print("Initialization end.")
# Evolution ########################################################################################################
while gen < gen_limit:
# parent selection ---------------------------------------------------------------------------------------------
#print("parent selection...")
parents = Selection.tournament_selection(init.population[1:],
mating_pool_size,
tournament_size)
#print("parent selection end.")
offsprings = []
i = 0
while len(offsprings
) < mating_pool_size: # 2 parents -> 2 individuals
p1 = parents[i]
p2 = parents[i + 1]
# crossover ------------------------------------------------------------------------------------------------
#print("crossover...")
if random.random() < xover_rate:
#off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1, off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
# mutation ------------------------------------------------------------------------------------------------
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
#off1 = Mutation.WGWRGM(p1, city_map)
#off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
#off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#off2 = Mutation.WGWRGM(p2, city_map)
#off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
#off2 = Mutation.inversion_mutation(p2, city_map)
#print("Mutation end")
offsprings.append(off1)
offsprings.append(off2)
#print(len(offsprings))
i += 2
# survial selection --------------------------------------------------------------------------------------------
#print("survival selection")
init.population[1:] = Selection.mu_plus_lambda(
init.population[1:], offsprings)
#print("survival selection end")
init.evalPopulation()
print("generation:", gen, " Average length:", init.AverageLength,
" Longest length: ", init.worstTour.length,
" shortest length:", init.bestTour.length)
# construct a generation #
generation = {
"best-individual-sequence":
init.bestTour.tour,
"best-individual-distance":
init.bestTour.length,
"worst-individual-sequence":
init.worstTour.tour,
"worst-individual-distance":
init.worstTour.length,
"average-distance":
init.AverageLength,
"individual-distances":
[round(distance.length) for distance in init.population
] # distance.length for distance in init.population
}
trial.append(generation)
gen += 1
trial[0]["time_cost"] = time.time() - start_time
experiment["evolutions"].append(trial)
# here processing generating json file #############################################################################
output_file = None
variable_declaration = "none"
if instance_name == "WesternSahara":
output_file = open("advanced_WesternSahara.js", "w")
variable_declaration = "let advanced_WesternSahara = "
if instance_name == "Uruguay":
output_file = open("advanced_Uruguay.js", "w")
variable_declaration = "let advanced_Uruguay = "
if instance_name == "Canada":
output_file = open("advanced_Canada.js", "w")
variable_declaration = "let advanced_Canada = "
output_file.write(variable_declaration)
output_file.write(json.dumps(experiment))
output_file.close()
def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 500
mating_pool_size = 100
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 15
print("Preparing information...")
c = CityMap.CityMap(western29 )
city_map = c.city_map
print("preparation end.")
gen = 0
init = Initialization.Population(popsize, city_map)
population = init.population
#EA algorithm
while gen < gen_limit:
#parent selection
#print("parent selection...")
parents = Selection.tournament_selection(population, mating_pool_size, tournament_size)
#print("parent selection end.")
offsprings = []
i = 0
while len(offsprings) < mating_pool_size:
p1 = parents[i][0]
p2 = parents[i + 1][0]
#crossover
#print("crossover...")
if random.random() < xover_rate:
off1, off2 = Crossover.COWGC(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
#mutation
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#print("Mutation end")
offsprings.append([off1, off1.fitness])
offsprings.append([off2, off2.fitness])
i += 2
#survial selection
#print("survival selection")
population = Selection.mu_plus_lambda(population, offsprings)
#print("survival selection end")
best_fitness = population[0][1]
best_tour = population[0][0].tour
tour_length = population[0][0].length
for i in population:
if i[0].length < tour_length:
best_fitness = i[1]
best_tour = i[0].tour
tour_length = i[0].length
print("generation: ", gen, " best fitness: ", best_fitness, " length: ", tour_length)
gen += 1