def moo(paras: List, hyperparameter: Dict, constants: Dict, paths: Dict):
init()
# define problem.
problem = Problem(num_of_variables=len(paras), objectives=[f1, f2, f3],
variables_range=paras,
preamble=JizhunPreamble(constants=constants, paths=paths))
evo = Evolution(
problem,
mutation_param=hyperparameter["MUTATION_PARAM"],
num_of_generations=hyperparameter["NUM_OF_GENERATIONS"],
num_of_individuals=hyperparameter["NUM_OF_INDIVIDUALS"],
num_of_tour_particips=hyperparameter["NUM_OF_TOUR_PARTICIPS"],
concurrency=hyperparameter["CONCURRENCY"],
max_proc=hyperparameter["MAX_PROC"])
# draw the last one with 3d box.
func = [i.objectives for i in evo.evolve()]
obj1 = [i[0] for i in func]
obj2 = [i[1] for i in func]
obj3 = [i[2] for i in func]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(obj1, obj2, obj3, c='r', marker='o')
plt.draw()
plt.savefig('results/epMOO_fig.png')
plt.show()
print("<Finished>{}".format(time.ctime()))
def moo_run(paras, hyperparameter, constants, paths):
"""The main entrance of the optimizer."""
init()
problem = Problem(num_of_variables=len(paras), objectives=[f1, f2, f3],
variables_range=paras,
preamble=Wkx2132Preamble(constants=constants, paths=paths))
evo = Evolution(
problem,
mutation_param=hyperparameter["MUTATION_PARAM"],
num_of_generations=hyperparameter["NUM_OF_GENERATIONS"],
num_of_individuals=hyperparameter["NUM_OF_INDIVIDUALS"],
num_of_tour_particips=hyperparameter["NUM_OF_TOUR_PARTICIPS"],
concurrency=hyperparameter["CONCURRENCY"],
max_proc=hyperparameter["MAX_PROC"])
# draw the last one with 3d box.
func = [i.objectives for i in evo.evolve()]
obj1 = [i[0] for i in func]
obj2 = [i[1] for i in func]
obj3 = [i[2] for i in func]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(obj1, obj2, obj3, c='r', marker='o')
plt.draw()
plt.savefig('results/epMOO_fig.png')
plt.show()
print("<Finished in {}, your patient is impressive, Congrads! Author: Jimmy Yao; From github.com/jummy233/epMOO>".format(time.ctime()))