Nelder Mead

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Nelder Mead#

This algorithm is implemented based on [14]. In addition to other implementations, a boundary check is included. This ensures that the search considers the box constraints of the given optimization problem. If no boundaries are provided, the algorithm falls back to a search without any constraints.

[1]:

from pymoo.algorithms.soo.nonconvex.nelder import NelderMead
from pymoo.problems import get_problem
from pymoo.optimize import minimize

problem = get_problem("sphere")

algorithm = NelderMead()

res = minimize(problem,
               algorithm,
               seed=1,
               verbose=False)

print("Best solution found: \nX = %s\nF = %s" % (res.X, res.F))

Best solution found:
X = [0.5007057  0.50020581 0.49999148 0.50029146 0.50000737 0.49950648
 0.50019156 0.49978683 0.50017533 0.49983613]
F = [1.00874476e-06]

API#

class pymoo.algorithms.soo.nonconvex.nelder.NelderMead(init_simplex_scale=0.05, func_params=<function adaptive_params>, output=<pymoo.util.display.single.SingleObjectiveOutput object>, **kwargs)[source]
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