def solve_sim(problem, problem_name):
start = timer()
best_state, best_fitness, fitness_curve = simulated_annealing(problem,
curve=True)
end = timer()
plot = plot_curve(problem_name + "-Simulated Annealing", "Iterations",
"Fitness", range(fitness_curve.size),
fitness_curve).show()
time = end - start
num_iters = fitness_curve.size
return best_fitness, time, num_iters, plot
print(end - start)
print(len(c))
times_ga.append(end - start)
fitns_ga.append(best_fitness)
# ________________________________________________________________________________________________________________________
# Define decay schedule
schedule = mlrose.ExpDecay()
times_sa = []
fitns_sa = []
# Solve problem using simulated annealing
for i in iterations:
start = time.time()
best_state, best_fitness, c = mlrose.simulated_annealing(problem_fit,
schedule=schedule,
max_attempts=10,
max_iters=i,
init_state=None,
curve=True,
random_state=3)
end = time.time()
print('sa algorithm @ {} iterations'.format(i))
print(best_fitness)
print(end - start)
print(len(c))
times_sa.append(end - start)
fitns_sa.append(best_fitness)
# ________________________________________________________________________________________________________________________
# Solve problem using random hill climb
times_rhc = []
fitns_rhc = []
for i in iterations: