def experiment_sa_4(self):
init_state = None
schedule_var = 1
best_state = None
max_attempts = np.array([5, 10, 15, 40, 60, 80, 100, 150, 200])
result = np.zeros((len(self.rand_seeds), len(max_attempts)))
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(max_attempts)):
prob_length = 20
max_iter = np.inf
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
max_attempt = max_attempts[j].item()
alg = SA(problem, init_state, rand_state, schedule_var,
max_attempt, max_iter)
best_state, best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('avg result ' + str(avg_result))
print('best_state')
print(best_state)
title = self.problem_type + ' with SA - Max Attempts Variation -Geom'
plot_curve(max_attempts, avg_result, title, 'Max Attempts',
'Best Score')
def experiment_sa_7(self):
init_state = None
schedule_var = 2
best_state = None
max_iters = np.arange(100, 5000, 100)
result = np.zeros((len(self.rand_seeds), len(max_iters)))
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(max_iters)):
prob_length = 20
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
max_attempt = 200
max_iter = max_iters[j].item()
alg = SA(problem, init_state, rand_state, schedule_var,
max_attempt, max_iter)
best_state, best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('best_state')
print(best_state)
print('avg result ' + str(avg_result))
title = self.problem_type + ' with SA - Max Iter Variation - Arith'
plot_curve(max_iters, avg_result, title, 'Max Iterations',
'Best Score')
def experiment_sa_11(self):
init_state = None
prob_lengths = np.arange(7, 30)
schedule_var = 0
best_state = None
result = np.zeros((len(self.rand_seeds), len(prob_lengths)))
best_state = None
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(prob_lengths)):
prob_length = prob_lengths[j]
fl = CustomProblem(prob_length.item(), self.problem_type)
problem = fl.create_problem()
alg = SA(problem, init_state, rand_state, schedule_var, 10,
1000)
best_state, best_fitness = alg.optimize()
result[i][j] = best_fitness
print(str(result))
print('best_state')
print(best_state)
avg_result = np.mean(result, axis=0)
print('avg result for varying input size' + str(avg_result))
title = self.problem_type + ' with SA - Input Size Variation'
plot_curve(prob_lengths, avg_result, title, 'Input Size', 'Best Score')
def experiment_optimal_sa(self):
prob_length = 20
init_state = None
schedule_var = 0
rand_state = 42
max_attempt = 110
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
start = time.time()
alg = SA(problem, init_state, rand_state, schedule_var, max_attempt,
1000)
best_score, best_fitness = alg.optimize()
end = time.time()
diff = abs(end - start)
print('time taken for SA - Knapsack: ' + str(diff))
def experiment_sa(self):
fl = FlipFlop(7)
problem = fl.create_problem()
alg = SA(problem, 42, 0, 10, 1000)
alg.optimize()
