from detpy.DETAlgs.base import BaseAlg
from detpy.DETAlgs.data.alg_data import EPSDEData
from detpy.DETAlgs.methods.methods_de import mutation, crossing
from detpy.DETAlgs.methods.methods_eps_de import selection, calculate_epsilon_constrained
from detpy.models.enums.basevectorschema import BaseVectorSchema
from detpy.models.enums.boundary_constrain import fix_boundary_constraints
from detpy.models.enums.crossingtype import CrossingType
[docs]
class EPSDE(BaseAlg):
"""
EPSDE - Epsilon Constrained Differential Evolution
Links:
https://ieeexplore.ieee.org/abstract/document/4274215
References:
Tetsuyuki Takahama; Setsuko Sakai; Noriyuki Iwane
"Solving Nonlinear Constrained Optimization Problems by the Epsilon Constrained Differential Evolution",
2006 IEEE International Conference on Systems, Man and Cybernetics,
08-11 October 2006, Taipei,Taiwan doi: 10.1109/ICSMC.2006.385209.
"""
def __init__(self, params: EPSDEData, db_conn=None, db_auto_write=False):
super().__init__(EPSDE.__name__, params, db_conn, db_auto_write)
self.mutation_factor = params.mutation_factor # F
self.crossover_rate = params.crossover_rate # Cr
self.g_funcs = params.g_funcs # Inequality constraints functions
self.h_funcs = params.h_funcs # Equality constraints functions
self.tolerance_h = params.tolerance_h
self.epsilon_level = params.epsilon_level
self.penalty_power = params.penalty_power
[docs]
def next_epoch(self):
pop_epsilon_constrained = calculate_epsilon_constrained(self._pop, self.g_funcs, self.h_funcs,
self.penalty_power, self.tolerance_h)
# New population after mutation
v_pop = mutation(self._pop, base_vector_schema=BaseVectorSchema.RAND,
optimization_type=self.optimization_type,
y=1,
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 = crossing(self._pop, v_pop, cr=self.crossover_rate, crossing_type=CrossingType.EXPOTENTIAL)
# Update values before selection
u_pop.update_fitness_values(self._function.eval, self.parallel_processing)
u_pop_epsilon_constrained = calculate_epsilon_constrained(u_pop, self.g_funcs, self.h_funcs, self.penalty_power,
self.tolerance_h)
# Select new population
new_pop = selection(self._pop, u_pop, pop_epsilon_constrained, u_pop_epsilon_constrained, self.epsilon_level)
# Override data
self._pop = new_pop