import numpy as np
[docs]class BP_mutation:
def __init__(
self,
lower_limits: np.ndarray,
upper_limits: np.ndarray,
ProM: float = None,
DisM: float = 20,
):
self.lower_limits = lower_limits
self.upper_limits = upper_limits
if ProM is None:
self.ProM = 1/len(lower_limits)
else:
self.ProM = ProM
self.DisM = DisM
[docs] def do(self, offspring: np.ndarray):
"""Conduct bounded polynomial mutation. Return the mutated individuals.
Parameters
----------
offspring : np.ndarray
The array of offsprings to be mutated.
Returns
-------
np.ndarray
The mutated offsprings
"""
min_val = np.ones_like(offspring) * self.lower_limits
max_val = np.ones_like(offspring) * self.upper_limits
k = np.random.random(offspring.shape)
miu = np.random.random(offspring.shape)
temp = np.logical_and((k <= self.ProM), (miu < 0.5))
offspring_scaled = (offspring - min_val) / (max_val - min_val)
offspring[temp] = offspring[temp] + (
(max_val[temp] - min_val[temp])
* (
(
2 * miu[temp]
+ (1 - 2 * miu[temp])
* (1 - offspring_scaled[temp]) ** (self.DisM + 1)
)
** (1 / (self.DisM + 1))
- 1
)
)
temp = np.logical_and((k <= self.ProM), (miu >= 0.5))
offspring[temp] = offspring[temp] + (
(max_val[temp] - min_val[temp])
* (
1
- (
2 * (1 - miu[temp])
+ 2 * (miu[temp] - 0.5) * offspring_scaled[temp] ** (self.DisM + 1)
)
** (1 / (self.DisM + 1))
)
)
offspring[offspring > max_val] = max_val[offspring > max_val]
offspring[offspring < min_val] = min_val[offspring < min_val]
return offspring
