mutation_03_de_movement

This function mutates a solution using a differential evolution mutation (rand/1).

x_i_new, of_i_new,\
    fit_i_new, neof = mutation_03_de_movement(obj_function,
                                                x_r0_old, x_r1_old,
                                                x_r2_old, 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_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_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

Use the mutation_03_de_movement function to generate a new solution from three existing solutions. Use the range \(\mathbf{x}_L = [1.0, 1.0]\) and \(\mathbf{x}_L = [5.0, 5.0]\). Consider current solutions \(\mathbf{x}_{r0} = [2.0, 3.0]\), \(\mathbf{x}_{r1} = [4.0, 5.0]\) and \(\mathbf{x}_{r2} = [3.6, 2.8]\). Use a scale factor equals 2.0.

# Import
from metapy_toolbox import mutation_03_de_movement # or import *

# Data

xR0 = [2.0, 3.0]
xR1 = [4.0, 5.0]
xR2 = [3.6, 2.8]
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_03_de_movement(objFunction, xR0, xR1, xR2, 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:  [2.48, 5.0]
of New:  31.1504
fit New:  0.031103812083208913
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 x0 = [2, 3]
    current x1 = [4, 5]
    current x2 = [3.6, 2.8]
    Dimension 0: rij = 0.3999999999999999, neighbor = 2.48
    Dimension 1: rij = 2.2, neighbor = 5.640000000000001
    update x = [2.48, 5.0], of = 31.1504, fit = 0.031103812083208913