from detpy.DETAlgs.base import BaseAlg
from detpy.DETAlgs.data.alg_data import EMDEData
from detpy.DETAlgs.methods.methods_de import binomial_crossing, selection
from detpy.DETAlgs.methods.methods_emde import em_mutation
from detpy.models.enums.boundary_constrain import fix_boundary_constraints
[docs]
class EMDE(BaseAlg):
"""
EMDE
Links:
https://link.springer.com/article/10.1007/s13042-015-0479-6
References:
Mohamed, A.W. An efficient modified differential evolution algorithm for solving constrained non-linear
integer and mixed-integer global optimization problems.
Int. J. Mach. Learn. & Cyber. 8, 989–1007 (2017). https://doi.org/10.1007/s13042-015-0479-6
"""
def __init__(self, params: EMDEData, db_conn=None, db_auto_write=False):
super().__init__(EMDE.__name__, params, db_conn, db_auto_write)
self.crossover_rate = params.crossover_rate # Cr
[docs]
def next_epoch(self):
# Calculate not constant cr depend on generation number
v_pop = em_mutation(self._pop)
# Apply boundary constrains on population in place
fix_boundary_constraints(v_pop, self.boundary_constraints_fun)
# New population after crossing
u_pop = binomial_crossing(self._pop, v_pop, self.crossover_rate)
# Update values before selection
u_pop.update_fitness_values(self._function.eval, self.parallel_processing)
# Select new population
new_pop = selection(self._pop, u_pop)
# Override data
self._pop = new_pop
self._epoch_number += 1