from detpy.DETAlgs.base import BaseAlg
from detpy.DETAlgs.data.alg_data import DEData
from detpy.DETAlgs.methods.methods_de import mutation, binomial_crossing, selection
from detpy.models.enums.boundary_constrain import fix_boundary_constraints
[docs]
class DE(BaseAlg):
"""
The original version of different evolution
Links:
https://link.springer.com/article/10.1023/A:1008202821328
References:
Storn, R., Price, K.
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces.
Journal of Global Optimization 11, 341–359 (1997).
https://doi.org/10.1023/A:1008202821328
"""
def __init__(self, params: DEData, db_conn=None, db_auto_write=False):
super().__init__(DE.__name__, params, db_conn, db_auto_write)
self.mutation_factor = params.mutation_factor # F
self.crossover_rate = params.crossover_rate # Cr
[docs]
def next_epoch(self):
# New population after mutation
v_pop = mutation(self._pop, f=self.mutation_factor)
# 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)
# Override data
self._pop = new_pop
self._epoch_number += 1