def getlastsolution(prob: Optimization):
new_index = prob.firstavailableindex(prob.getSolSet())
return prob.getSol(new_index - 1)
x = xdict['x']
y = xdict['y']
ff = [
(x - 0.0)**2 + (y - 0.0)**2,
(x - 1.0)**2 + (y - 1.0)**2,
]
gg = []
fail = False
return ff, gg, fail
# Instantiate Optimization Problem
optProb = Optimization('Rosenbrock function', objfunc, use_groups=True)
optProb.addVar('x', 'c', value=0, lower=-600, upper=600)
optProb.addVar('y', 'c', value=0, lower=-600, upper=600)
optProb.addObj('obj1')
optProb.addObj('obj2')
# 300 generations will find x=(0,0), 200 or less will find x=(1,1)
options = {
'maxGen': 200,
}
opt = NSGA2(options=options)
opt.setOption('PrintOut', 0)
opt(optProb)
print(optProb.getSol(0))
