124 lines
3.4 KiB
Python
124 lines
3.4 KiB
Python
import random
|
|
import numpy
|
|
import time
|
|
from .solution import solution
|
|
|
|
|
|
# Differential Evolution (DE)
|
|
# mutation factor = [0.5, 2]
|
|
# crossover_ratio = [0,1]
|
|
def DE(objf, lb, ub, dim, PopSize, iters):
|
|
|
|
mutation_factor = 0.5
|
|
crossover_ratio = 0.7
|
|
stopping_func = None
|
|
|
|
# convert lb, ub to array
|
|
if not isinstance(lb, list):
|
|
lb = [lb for _ in range(dim)]
|
|
ub = [ub for _ in range(dim)]
|
|
|
|
# solution
|
|
s = solution()
|
|
|
|
s.best = float("inf")
|
|
|
|
# initialize population
|
|
population = []
|
|
|
|
population_fitness = numpy.array([float("inf") for _ in range(PopSize)])
|
|
|
|
for p in range(PopSize):
|
|
sol = []
|
|
for d in range(dim):
|
|
d_val = random.uniform(lb[d], ub[d])
|
|
sol.append(d_val)
|
|
|
|
population.append(sol)
|
|
|
|
population = numpy.array(population)
|
|
|
|
# calculate fitness for all the population
|
|
for i in range(PopSize):
|
|
fitness = objf(population[i, :])
|
|
population_fitness[p] = fitness
|
|
# s.func_evals += 1
|
|
|
|
# is leader ?
|
|
if fitness < s.best:
|
|
s.best = fitness
|
|
s.leader_solution = population[i, :]
|
|
|
|
convergence_curve = numpy.zeros(iters)
|
|
# start work
|
|
print('DE is optimizing "' + objf.__name__ + '"')
|
|
|
|
timerStart = time.time()
|
|
s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
|
|
|
|
t = 0
|
|
while t < iters:
|
|
# should i stop
|
|
if stopping_func is not None and stopping_func(s.best, s.leader_solution, t):
|
|
break
|
|
|
|
# loop through population
|
|
for i in range(PopSize):
|
|
# 1. Mutation
|
|
|
|
# select 3 random solution except current solution
|
|
ids_except_current = [_ for _ in range(PopSize) if _ != i]
|
|
id_1, id_2, id_3 = random.sample(ids_except_current, 3)
|
|
|
|
mutant_sol = []
|
|
for d in range(dim):
|
|
d_val = population[id_1, d] + mutation_factor * (
|
|
population[id_2, d] - population[id_3, d]
|
|
)
|
|
|
|
# 2. Recombination
|
|
rn = random.uniform(0, 1)
|
|
if rn > crossover_ratio:
|
|
d_val = population[i, d]
|
|
|
|
# add dimension value to the mutant solution
|
|
mutant_sol.append(d_val)
|
|
|
|
# 3. Replacement / Evaluation
|
|
|
|
# clip new solution (mutant)
|
|
mutant_sol = numpy.clip(mutant_sol, lb, ub)
|
|
|
|
# calc fitness
|
|
mutant_fitness = objf(mutant_sol)
|
|
# s.func_evals += 1
|
|
|
|
# replace if mutant_fitness is better
|
|
if mutant_fitness < population_fitness[i]:
|
|
population[i, :] = mutant_sol
|
|
population_fitness[i] = mutant_fitness
|
|
|
|
# update leader
|
|
if mutant_fitness < s.best:
|
|
s.best = mutant_fitness
|
|
s.leader_solution = mutant_sol
|
|
|
|
convergence_curve[t] = s.best
|
|
if t % 1 == 0:
|
|
print(
|
|
["At iteration " + str(t + 1) + " the best fitness is " + str(s.best)]
|
|
)
|
|
|
|
# increase iterations
|
|
t = t + 1
|
|
|
|
timerEnd = time.time()
|
|
s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
|
|
s.executionTime = timerEnd - timerStart
|
|
s.convergence = convergence_curve
|
|
s.optimizer = "DE"
|
|
s.bestIndividual = s.leader_solution
|
|
s.objfname = objf.__name__
|
|
|
|
# return solution
|
|
return s
|