Success-History based Adaptive Differential Evolution (SHADE)

class pypop7.optimizers.de.shade.SHADE(problem, options)[source]

Success-History based Adaptive Differential Evolution (SHADE).

Parameters:

  • problem (dict) –

    problem arguments with the following common settings (keys):

    • ’fitness_function’ - objective function to be minimized (func),

    • ’ndim_problem’ - number of dimensionality (int),

    • ’upper_boundary’ - upper boundary of search range (array_like),

    • ’lower_boundary’ - lower boundary of search range (array_like).

  • options (dict) –

    optimizer options with the following common settings (keys):

    • ’max_function_evaluations’ - maximum of function evaluations (int, default: np.inf),

    • ’max_runtime’ - maximal runtime to be allowed (float, default: np.inf),

    • ’seed_rng’ - seed for random number generation needed to be explicitly set (int);

    and with the following particular settings (keys):

    • ’n_individuals’ - number of offspring, aka offspring population size (int, default: 100),

    • ’mu’ - mean of normal distribution for adaptation of crossover probability (float, default: 0.5),

    • ’median’ - median of Cauchy distribution for adaptation of mutation factor (float, default: 0.5),

    • ’h’ - length of historical memory (int, default: 100).

Examples

Use the optimizer to minimize the well-known test function Rosenbrock:

 1>>> import numpy
 2>>> from pypop7.benchmarks.base_functions import rosenbrock  # function to be minimized
 3>>> from pypop7.optimizers.de.shade import SHADE
 4>>> problem = {'fitness_function': rosenbrock,  # define problem arguments
 5...            'ndim_problem': 2,
 6...            'lower_boundary': -5*numpy.ones((2,)),
 7...            'upper_boundary': 5*numpy.ones((2,))}
 8>>> options = {'max_function_evaluations': 5000,  # set optimizer options
 9...            'seed_rng': 0}
10>>> shade = SHADE(problem, options)  # initialize the optimizer class
11>>> results = shade.optimize()  # run the optimization process
12>>> # return the number of function evaluations and best-so-far fitness
13>>> print(f"SHADE: {results['n_function_evaluations']}, {results['best_so_far_y']}")
14SHADE: 5000, 6.231767087114823e-05

For its correctness checking of coding, refer to this code-based repeatability report for more details.

h

length of historical memory.

Type:

int

median

median of Cauchy distribution for adaptation of mutation factor.

Type:

float

mu

mean of normal distribution for adaptation of crossover probability.

Type:

float

n_individuals

number of offspring, aka offspring population size.

Type:

int

References

Tanabe, R. and Fukunaga, A., 2013, June. Success-history based parameter adaptation for differential evolution. In IEEE Congress on Evolutionary Computation (pp. 71-78). IEEE.