def optimize(model,
scenario,
nfe,
epsilons,
sc_name,
algorithm=EpsNSGAII,
searchover='levers'):
'''optimize the model
Parameters
----------
model : a Model instance
algorith : a valid Platypus optimization algorithm
nfe : int
searchover : {'uncertainties', 'levers'}
Returns
-------
pandas DataFrame
Raises
------
EMAError if searchover is not one of 'uncertainties' or 'levers'
TODO:: constrains are not yet supported
'''
if searchover not in ('levers', 'uncertainties'):
raise EMAError(("searchover should be one of 'levers' or"
"'uncertainties' not {}".format(searchover)))
# extract the levers and the outcomes
decision_variables = [dv for dv in getattr(model, searchover)]
outcomes = [
outcome for outcome in model.outcomes
if outcome.kind != AbstractOutcome.INFO
]
evalfunc = functools.partial(evaluate_function,
model=model,
scenario=scenario,
decision_vars=decision_variables,
searchover=searchover)
# setup the optimization problem
# TODO:: add constraints
problem = Problem(len(decision_variables), len(outcomes))
problem.types[:] = [
Real(dv.lower_bound, dv.upper_bound) for dv in decision_variables
]
problem.function = evalfunc
problem.directions = [outcome.kind for outcome in outcomes]
# solve the optimization problem
optimizer = algorithm(problem, epsilons=epsilons)
optimizer.run(nfe)
# extract the names for levers and the outcomes
lever_names = [dv.name for dv in decision_variables]
outcome_names = [outcome.name for outcome in outcomes]
solutions = []
for solution in unique(nondominated(optimizer.result)):
decision_vars = dict(zip(lever_names, solution.variables))
decision_out = dict(zip(outcome_names, solution.objectives))
result = {**decision_vars, **decision_out}
solutions.append(result)
#print("fe_result: ", optimizer.algorithm.fe_results)
#plot_convergence(optimizer.algorithm.hv_results, sc_name)
results = pd.DataFrame(solutions, columns=lever_names + outcome_names)
#save the hypervolume output in a csv file
hv = np.swapaxes(
np.array(optimizer.algorithm.hv_results), 0, 1
) #hv is a 2d list, where hv[0] is the record of nfe's, hv[1] is the record of hypervolume
df = pd.DataFrame(hv).transpose()
#df.to_csv("Hypervolume_scenario_{}_v6.csv".format(sc_name))
return results, df
def cubicTraj(values):
theta0 = 15
thetaf = 75
t0 = 0
tf = values[0]
a3 = (-2/tf**3)*(thetaf - theta0)
a2 = (3/tf**2)*(thetaf - theta0)
a1 = 0
a0 = theta0
jerk = np.sqrt((6*a3)**2)
return [tf,jerk]
problem = Problem(1,2)
problem.types[:] = Real(3,20)
problem.directions = [-1,-1]
problem.function = cubicTraj
algorithm = NSGAII(problem)
algorithm.run(10000)
tf_opt = [s.objectives[0] for s in algorithm.result]
jerk_opt = [s.objectives[1] for s in algorithm.result]
plt.scatter(tf_opt,jerk_opt)
plt.show()
tf = 5.23705
jerk = 4.96
