# # bus[pv, 15] = 0
# dc_bus_sol, _, _ = loadflow_dc_pro(bus, line, printout=False)
# dc_ThetaMean = Theta_abs_mean(dc_bus_sol)
# print(dc_ThetaMean)
# return dc_ThetaMean
# Define fitness function.
@engine.fitness_register
def fitness(indv):
x = np.array(indv.solution)
# y = -float(sum(x**2 - 10 * np.cos(2*np.pi*x) + 10))
bus[pv, 15] = np.rint(x)
# bus[pv, 15] = 0
dc_bus_sol, _, _ = loadflow_dc_pro(bus, line, printout=False)
dc_ThetaMean = Theta_abs_mean(dc_bus_sol)
print(dc_ThetaMean)
return -float(dc_ThetaMean)
if '__main__' == __name__:
time_start = time()
engine.run(ng=generations)
time_end = time()
print("GA costs %.4f seconds!" % (time_end - time_start))
best_indv = engine.population.best_indv(engine.fitness)
print(best_indv.solution)
print(-engine.fitness(best_indv))
best_population_point = best_indv.solution
bus[pv, 15] = np.rint(best_population_point)
converge, bus_sol, line_f_from, line_f_to = loadflow(bus, line, printout=True)
self.logger.info(msg)
def finalize(self, population, engine):
best_indv = population.best_indv(engine.fitness)
x = best_indv.solution
y = engine.ori_fmax
msg = 'Optimal solution: ({}, {})'.format(x, y)
self.logger.info(msg)
if '__main__' == __name__:
# Run the GA engine and print every generation
engine.run(ng=500)
best_indv = engine.population.best_indv(engine.fitness)
print('Max({0},{1})'.format(best_indv.solution[0],
engine.fitness(best_indv)))
x = np.linspace(0, 15, 10000)
y = [-3 * (i - 30)**2 * math.sin(i) for i in x]
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.title('function')
plt.axis([-1, 16, -3000, 3000])
plt.scatter(best_indv.solution[0], engine.fitness(best_indv), color='r')
a = round(best_indv.solution[0], 4)
b = round(engine.fitness(best_indv), 4)
plt.annotate('Max(' + str(a) + ',' + str(b) + ')',
xy=(best_indv.solution[0], engine.fitness(best_indv)),
xytext=(7, 2500),
arrowprops=dict(facecolor='black', shrink=0.1, width=2))
plt.show()
