tuning_ncycle_calc.py

import pygmo as pg
from utils.pygmo_utils import calculate_mean_rbf
from utils.utils import save_values

# where n is the number of times the meta-heuristic algorithms are run to get the mean
n = 10
dim = 30
problem_name = 'zdt'
problem_number = 6

max_fevals = (dim + 1) * 50

# 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

default_rf = 3

# For the each problem in the problem suite
for i in range(5, problem_number):
    # Skip problem 5
    if i == 4:
        continue

    problem_function = getattr(pg.problems, problem_name)

    problem = pg.problem(problem_function(i+1, param=dim))

    for cycle_mult in range(3):
        cycle = (cycle_mult+1) * default_rf
        hv_rbfmopt_plot = calculate_mean_rbf(n, max_fevals, working_fevals, seed, problem, cycle, None)
        save_values('store_hv/rbfmopt_hv_cycle' + str(cycle) + '_' + problem.get_name() + '_fevals' + str(max_fevals) + '.txt', hv_rbfmopt_plot.tolist())