def upper_bound(self) -> np.ndarray:
upper_bounds = [
list(benchmark.upper_bound) for benchmark in self.benchmarks
]
upper_bound = list(itertools.chain(*upper_bounds))
upper_bound = np.array(upper_bound)
if self.USE_SHUFFLE:
upper_bound = shuffle(upper_bound, self.gene_order)
return upper_bound
def lower_bound(self) -> np.ndarray:
lower_bounds = [
list(benchmark.lower_bound) for benchmark in self.benchmarks
]
lower_bound = list(itertools.chain(*lower_bounds))
lower_bound = np.array(lower_bound)
if self.USE_SHUFFLE:
lower_bound = shuffle(lower_bound, self.gene_order)
return lower_bound
def random_solutions(self, population_size: int) -> List[Solution]:
populations = []
for benchmark in self.benchmarks:
population = benchmark.initialize_population(population_size)
populations.append(population.as_ndarray)
genomes = np.concatenate(populations, axis=1)
if self.USE_SHUFFLE:
genomes = shuffle(genomes, self.gene_order)
return list(Solution(genome) for genome in genomes)
def random_solutions(self, population_size: int) -> List[Solution]:
genomes = self.random_state.uniform(low=0,
high=1,
size=(population_size,
self.genome_size))
genomes *= self.bound_range
genomes += self.lower_bound
if self.USE_SHUFFLE:
genomes = shuffle(genomes, self.gene_order)
return list(Solution(genome) for genome in genomes)
def random_solutions(self, population_size: int) -> List[Solution]:
target_nodes_idx = [node.id for node in self.config.target_nodes]
max_id = max(target_nodes_idx)
n = len(target_nodes_idx)
depots = range(max_id + 1, max_id + self.genome_size - n + 1)
nodes_idx = target_nodes_idx + list(depots)
genomes = []
for _ in range(population_size):
genome = self.random_state.permutation(nodes_idx)
genomes.append(genome)
genomes = np.array(genomes)
if self.USE_SHUFFLE:
genomes = shuffle(genomes, self.gene_order)
return list(Solution(genome) for genome in genomes)