Welcome to pyDE’s documentation!¶
de¶
de package¶
Submodules¶
de.optimization¶
This module contains the core Differential Evolution calculations.
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de.optimization.optimize(fobj, dim, low_limit, high_limit, N=100, max_number_of_generations=2000, mutation_parameter=0.9, scale_factor=0.5, seed=974378)[source]¶ Differential Evolution calculations. This routine computes a minimum of a given objective function. The actual method is only valid for unconstrained optimization problems.
Parameters: - fobj (function) – The objective function.
- dim (int) – Number of dimensions of the objective function’s argument.
- low_limit (float) – The inferior limit of the hypercube search region.
- high_limit (float) – The superior limit of the hypercube search region.
- N (int) – The number of individuals to be generated.
- max_number_of_generations (int) – Max number of generations to be employed by the procedure.
- mutation_parameter (float) – A parameter to related to the success’ rate of mutations.
- scale_factor (float) – A scale factor of linear combination employed in the mutation procedure.
- seed (int) – A seed to be employed in the pseudo-random numbers generation.
Returns: The solution coordinates, the objective function evaluated at this point, the method convergence’s flag and the output log message.
Return type: tuple
de.benchmarks¶
Provides some benchmark problems to global optimization.
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de.benchmarks.f_ackley(x, a, b, c)[source]¶ Define the benchmark Ackley function.
Parameters: - x (numpy.ndarray) – The function’s argument array.
- a (float) – Function’s constant.
- b (float) – Function’s constant.
- c (float) – Function’s constant.
Returns: The evaluated function at the given input array.
Return type: float