# where n is the number of times the meta-heuristic algorithms are run to get the mean n = 1 problem_name = 'zdt' problem_number = 6 # To account for the fact that the zero index array in util functions # are actually the 1st feval pop_size = 24 seed = 33 default_rf = 3 # i is the problem number chosen i = 4 dim = 30 problem_function = getattr(pg.problems, problem_name) problem = pg.problem(problem_function(i, param=dim)) max_fevals = 110 working_fevals = max_fevals-1 file_string = '/Users/rogerko/dev/Opossum/benchmark/csv/benchmark_problem' + str(i) + '_dvar' + str(dim) + '.txt' stream = open(file_string, 'a') hv_rbfmopt_plot = calculate_mean_rbf(n, max_fevals, working_fevals, seed, problem, default_rf, output_stream=stream) stream.close() csv_file_string = '/Users/rogerko/dev/Opossum/benchmark/csv/benchmark_problem' + str(i) + '_dvar' + str(dim) + '.csv' # load the txt file and gen csv gen_csv(file_string, csv_file_string)
working_fevals = max_fevals - 1
pop_size = 24
seed = 33
# For the each problem in the problem suite
for i in range(problem_number):
problem_function = getattr(pg.problems, problem_name)
if (problem_name == "dtlz"):
problem = pg.problem(problem_function(i + 1, dim=dim, fdim=fdim))
else:
problem = pg.problem(problem_function(i + 1, param=dim))
algo_moead = pg.algorithm(pg.moead(gen=1))
algo_nsga2 = pg.algorithm(pg.nsga2(gen=1))
# Hypervolume calculations, mean taken over n number of times
hv_rbfmopt_plot = calculate_mean_rbf(n, max_fevals, working_fevals, seed,
problem, cycle)
hv_moead_plot = calculate_mean_pyg(n, algo_moead, working_fevals, pop_size,
seed, problem)
hv_nsga2_plot = calculate_mean_pyg(n, algo_nsga2, working_fevals, pop_size,
seed, problem)
fevals_plot = range(0, max_fevals)
save_values(
'storedvalues/rbfmopt_hv_' + problem.get_name() + '_fevals' +
str(max_fevals) + '.txt', hv_rbfmopt_plot.tolist())
save_values(
'storedvalues/moead_hv_' + problem.get_name() + '_fevals' +
str(max_fevals) + '.txt', hv_moead_plot.tolist())
save_values(
'storedvalues/nsga2_hv_' + problem.get_name() + '_fevals' +
str(max_fevals) + '.txt', hv_nsga2_plot.tolist())
problem_number = 6
# max_fevals = (dim+1) * 50
max_fevals = 30
# To account for the fact that the zero index array in util functions
# are actually the 1st feval
working_fevals = max_fevals-1
pop_size = 24
seed = 33
cycle = 3
max_filter = 3
# For the each problem in the problem suite
for i in range(problem_number):
if i == 4:
continue
problem_function = getattr(pg.problems, problem_name)
problem = pg.problem(problem_function(i+1, param=dim))
hv_rbfmopt_plot = calculate_mean_rbf(n, max_fevals, working_fevals, seed, problem, cycle, None)
save_values('store_hv/rbfmopt_hv_ncycle' + str(cycle) + '_filter' + 'None' + '_fevals' + str(max_fevals) + problem.get_name() + '.txt', hv_rbfmopt_plot.tolist())
for j in range(3):
hv_rbfmopt_plot = calculate_mean_rbf(n, max_fevals, working_fevals, seed, problem, cycle, (j+1)*max_filter*dim)
save_values('store_hv/rbfmopt_hv_ncycle' + str(cycle) + '_filter' + str((j+1)*max_filter*dim) + '_fevals' + str(max_fevals) + problem.get_name() + '.txt', hv_rbfmopt_plot.tolist())