mutation_07_de_movement

This function mutates a solution using a differential evolution mutation (current-to-best/1).

x_i_new, of_i_new,\
    fit_i_new, neof = mutation_07_de_movement(obj_function, x_r0_old,
                                                x_r1_old, x_r2_old,
                                                x_best, x_lower,
                                                x_upper, n_dimensions,
                                                f, none_variable=None)

Input variables

Name	Description	Type
obj_function	Objective function. The Metapy user defined this function	Py function (def)
x_i_old	Current design variables of the \(i\) agent	List
x_r0_old	Current design variables of the random \(r_0\) agent	List
x_r1_old	Current design variables of the random \(r_1\) agent	List
x_r2_old	Current design variables of the random \(r_2\) agent	List
x_best	Best design variables from the population	List
x_lower	Lower limit of the design variables	List
x_upper	Upper limit of the design variables	List
n_dimensions	Problem dimension	Integer
f	Scaling factor	Float
none_variable	None variable. Default is None. User can use this variable in objective function	None, list, float, dictionary, str or any

Output variables

Name	Description	Type
x_i_new	Update variables of the \(i\) agent	List
of_i_new	Update objective function value of the \(i\) agent	Float
fit_i_new	Update fitness value of the \(i\) agent	Float
neof	Number of evaluations of the objective function	Integer
report	Report about the mutation process	String

Example 1

# Import
from metapy_toolbox import mutation_07_de_movement # or import *

# Data
xI = [2.0, 2.0]
xR0 = [1.4, 2.6]
xR1 = [3.1, 4.7]
xR2 = [2.1, 4.9]
xBest = [1.2, 1.2]
xL = [1.0, 1.0]
xU = [5.0, 5.0]
d = len(xL)
f = 1.2

# Objective function

def objFunction(x, _):
    """Example objective function"""
    x0 = x[0]
    x1 = x[1]
    of = x0 ** 2 + x1 ** 2
    return of

# Call function

xNew, ofNew, fitNew, neof, report = mutation_07_de_movement(objFunction, xI, xR0, xR1, xR3, xBest, xL, xU, d, f)

# Output details

print('x New: ', xNew)
print('of New: ', ofNew)
print('fit New: ', fitNew)
print('number of evalutions objective function: ', neof)

x New:  [1.8800000000000001, 1.1600000000000001]
of New:  4.880000000000001
fit New:  0.17006802721088432
number of evalutions objective function:  1

To check the movement report just apply the following instruction.

# Report details
arq = "report_example.txt"

# Writing report
with open(arq, "w") as file:
    file.write(report)

Open report_example.txt.

    current xi = [2.0, 2.0]
    current xr0 = [1.4, 2.6]
    current xr1 = [3.1, 4.7]
    current xr2 = [3.0, 4.0]
    current x_best = [1.2, 1.2]
    Dimension 0: rij_1 = -0.19999999999999996, rij_2 = 0.10000000000000009, neighbor = 1.8800000000000001
    Dimension 1: rij_1 = -1.4000000000000001, rij_2 = 0.7000000000000002, neighbor = 1.1600000000000001
    update x = [1.8800000000000001, 1.1600000000000001], of = 4.880000000000001, fit = 0.17006802721088432