from detpy.DETAlgs.base import BaseAlg
from detpy.DETAlgs.data.alg_data import DEGLData
from detpy.DETAlgs.methods.methods_de import binomial_crossing, selection
from detpy.DETAlgs.methods.methods_degl import degl_mutation, degl_adapt_weight
from detpy.models.enums.boundary_constrain import fix_boundary_constraints
[docs]
class DEGL(BaseAlg):
"""
DEGL
Links:
https://ieeexplore.ieee.org/document/5089881/
References:
S. Das, A. Abraham, U. K. Chakraborty and A. Konar,
"Differential Evolution Using a Neighborhood-Based Mutation Operator,"
in IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 526-553, June 2009,
doi: 10.1109/TEVC.2008.2009457.
"""
def __init__(self, params: DEGLData, db_conn=None, db_auto_write=False):
super().__init__(DEGL.__name__, params, db_conn, db_auto_write)
self.mutation_factor = params.mutation_factor # F
self.crossover_rate = params.crossover_rate # Cr
self.radius = params.radius
self.weight = 0
[docs]
def next_epoch(self):
# New population after mutation
v_pop = degl_mutation(self._pop, self.radius, self.mutation_factor, self.weight)
# 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, cr=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)
self.weight = degl_adapt_weight(self._epoch_number, self.num_of_epochs)
# Override data
self._pop = new_pop
self._epoch_number += 1