ISRES

class ISRES(max_evals=1000)

Improved Stochastic Ranking Evolution Strategy optimizer.

Improved Stochastic Ranking Evolution Strategy (ISRES) is an algorithm for non-linearly constrained global optimization. It has heuristics to escape local optima, even though convergence to a global optima is not guaranteed. The evolution strategy is based on a combination of a mutation rule and differential variation. The fitness ranking is simply via the objective function for problems without nonlinear constraints. When nonlinear constraints are included, the stochastic ranking proposed by Runarsson and Yao is employed. This method supports arbitrary nonlinear inequality and equality constraints, in addition to the bound constraints.

NLopt global optimizer, derivative-free. For further detail, please refer to http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#isres-improved-stochastic-ranking-evolution-strategy

Parameters

max_evals (int) – Maximum allowed number of function evaluations.

Raises

NameError – NLopt library not installed.

Attributes

ISRES.bounds_support_level	Returns bounds support level
ISRES.gradient_support_level	Returns gradient support level
ISRES.initial_point_support_level	Returns initial point support level
ISRES.is_bounds_ignored	Returns is bounds ignored
ISRES.is_bounds_required	Returns is bounds required
ISRES.is_bounds_supported	Returns is bounds supported
ISRES.is_gradient_ignored	Returns is gradient ignored
ISRES.is_gradient_required	Returns is gradient required
ISRES.is_gradient_supported	Returns is gradient supported
ISRES.is_initial_point_ignored	Returns is initial point ignored
ISRES.is_initial_point_required	Returns is initial point required
ISRES.is_initial_point_supported	Returns is initial point supported
ISRES.setting	Return setting

Methods

ISRES.get_nlopt_optimizer()	Return NLopt optimizer type
ISRES.get_support_level()	return support level dictionary
ISRES.gradient_num_diff(x_center, f, epsilon)	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
ISRES.optimize(num_vars, objective_function)	Perform optimization.
ISRES.print_options()	Print algorithm-specific options.
ISRES.set_max_evals_grouped(limit)	Set max evals grouped
ISRES.set_options(**kwargs)	Sets or updates values in the options dictionary.
ISRES.wrap_function(function, args)	Wrap the function to implicitly inject the args at the call of the function.