def test_knapsack_genetic_algorithm_low_dimensional_1():
params = open_file("../dataset/low-dimensional/f1_l-d_kp_10_269")
knapsack = GeneticKnapsack(params[0], params[1])
result = knapsack.solve()
optimum = int((open(
"../dataset/low-dimensional-optimum/f1_l-d_kp_10_269").readline()))
result = sorted(result, key=lambda item: item.fitness, reverse=True)
assert optimum == 295
assert result[0].fitness.values[0] == optimum
def test_knapsack_genetic_algorithm_large_scale_1():
params = open_file("../dataset/large_scale/knapPI_1_100_1000_1")
knapsack = GeneticKnapsack(params[0], params[1])
result = knapsack.solve(300, 100, mu=400, l=1600)
optimum = int((
open("../dataset/large_scale-optimum/knapPI_1_100_1000_1").readline()))
result = sorted(result, key=lambda item: item.fitness, reverse=True)
assert optimum == 9147
assert result[0].fitness.values[0] <= 8500
assert result[0].fitness.values[0] >= 7000
def test_knapsack_simulated_annealing_large_scale_1():
params = open_file("../dataset/large_scale/knapPI_1_100_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing(ins_problem, exp_schedule(1000, 0.0005, 4000))
optimum = int((
open("../dataset/large_scale-optimum/knapPI_1_100_1000_1").readline()))
assert optimum == 9147
assert result.value <= optimum
assert result.value >= 8000
def test_knapsack_simulated_annealing_low_dimensional_2():
params = open_file("../dataset/low-dimensional/f2_l-d_kp_20_878")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = simulated_annealing(ins_problem,
exp_schedule(2000, 0.00005, 200000))
optimum = int((open(
"../dataset/low-dimensional-optimum/f2_l-d_kp_20_878").readline()))
assert optimum == 1024
assert result.value == optimum