/**
* @brief Creates a rounded normalized version of the given solution w.r.t. the given ROI.
*
* If the solution seems to be better than the extremes it is corrected (2 objectives are assumed).
* The caller is responsible for freeing the allocated memory using coco_free_memory().
*/
static double *mo_normalize(const double *y, const double *ideal, const double *nadir, const size_t num_obj) {
size_t i;
double *normalized_y = coco_allocate_vector(num_obj);
for (i = 0; i < num_obj; i++) {
assert((nadir[i] - ideal[i]) > mo_discretization);
normalized_y[i] = (y[i] - ideal[i]) / (nadir[i] - ideal[i]);
normalized_y[i] = coco_double_round(normalized_y[i] / mo_discretization) * mo_discretization;
if (normalized_y[i] < 0) {
coco_debug("Adjusting %.15e to %.15e", y[i], ideal[i]);
normalized_y[i] = 0;
}
}
for (i = 0; i < num_obj; i++) {
assert(num_obj == 2);
if (coco_double_almost_equal(normalized_y[i], 0, mo_precision) && (normalized_y[1-i] < 1)) {
coco_debug("Adjusting %.15e to %.15e", y[1-i], nadir[1-i]);
normalized_y[1-i] = 1;
}
}
return normalized_y;
}
/**
* @brief Creates the transformation.
*/
static coco_problem_t *transform_vars_affine(coco_problem_t *inner_problem,
const double *M,
const double *b,
const size_t number_of_variables) {
/*
* TODO:
* - Calculate new smallest/largest values of interest?
* - Resize bounds vectors if input and output dimensions do not match
*/
coco_problem_t *problem;
transform_vars_affine_data_t *data;
size_t entries_in_M;
entries_in_M = inner_problem->number_of_variables * number_of_variables;
data = (transform_vars_affine_data_t *) coco_allocate_memory(sizeof(*data));
data->M = coco_duplicate_vector(M, entries_in_M);
data->b = coco_duplicate_vector(b, inner_problem->number_of_variables);
data->x = coco_allocate_vector(inner_problem->number_of_variables);
problem = coco_problem_transformed_allocate(inner_problem, data, transform_vars_affine_free, "transform_vars_affine");
problem->evaluate_function = transform_vars_affine_evaluate;
if (coco_problem_best_parameter_not_zero(inner_problem)) {
coco_debug("transform_vars_affine(): 'best_parameter' not updated, set to NAN");
coco_vector_set_to_nan(inner_problem->best_parameter, inner_problem->number_of_variables);
}
return problem;
}
/**
* @brief Computes the instance number of the second problem/objective so that the resulting bi-objective
* problem has more than a single optimal solution.
*
* Starts by setting instance2 = instance1 + 1 and increases this number until an appropriate instance has
* been found (or until a maximum number of tries has been reached, in which case it throws a coco_error).
* An appropriate instance is the one for which the resulting bi-objective problem (in any considered
* dimension) has the ideal and nadir points apart enough in the objective space and the extreme optimal
* points apart enough in the decision space. When the instance has been found, it is output through
* coco_warning, so that the user can see it and eventually manually add it to suite_biobj_instances.
*/
static size_t suite_biobj_get_new_instance(coco_suite_t *suite,
const size_t instance,
const size_t instance1,
const size_t num_bbob_functions,
const size_t *bbob_functions) {
size_t instance2 = 0;
size_t num_tries = 0;
const size_t max_tries = 1000;
const double apart_enough = 1e-4;
int appropriate_instance_found = 0, break_search, warning_produced = 0;
coco_problem_t *problem1, *problem2, *problem = NULL;
size_t d, f1, f2, i;
size_t function1, function2, dimension;
double norm;
suite_biobj_t *data;
assert(suite->data);
data = (suite_biobj_t *) suite->data;
while ((!appropriate_instance_found) && (num_tries < max_tries)) {
num_tries++;
instance2 = instance1 + num_tries;
break_search = 0;
/* An instance is "appropriate" if the ideal and nadir points in the objective space and the two
* extreme optimal points in the decisions space are apart enough for all problems (all dimensions
* and function combinations); therefore iterate over all dimensions and function combinations */
for (f1 = 0; (f1 < num_bbob_functions) && !break_search; f1++) {
function1 = bbob_functions[f1];
for (f2 = f1; (f2 < num_bbob_functions) && !break_search; f2++) {
function2 = bbob_functions[f2];
for (d = 0; (d < suite->number_of_dimensions) && !break_search; d++) {
dimension = suite->dimensions[d];
if (dimension == 0) {
if (!warning_produced)
coco_warning("suite_biobj_get_new_instance(): remove filtering of dimensions to get generally acceptable instances!");
warning_produced = 1;
continue;
}
problem1 = coco_get_bbob_problem(function1, dimension, instance1);
problem2 = coco_get_bbob_problem(function2, dimension, instance2);
if (problem) {
coco_problem_stacked_free(problem);
problem = NULL;
}
problem = coco_problem_stacked_allocate(problem1, problem2);
/* Check whether the ideal and nadir points are too close in the objective space */
norm = mo_get_norm(problem->best_value, problem->nadir_value, 2);
if (norm < 1e-1) { /* TODO How to set this value in a sensible manner? */
coco_debug(
"suite_biobj_get_new_instance(): The ideal and nadir points of %s are too close in the objective space",
problem->problem_id);
coco_debug("norm = %e, ideal = %e\t%e, nadir = %e\t%e", norm, problem->best_value[0],
problem->best_value[1], problem->nadir_value[0], problem->nadir_value[1]);
break_search = 1;
}
/* Check whether the extreme optimal points are too close in the decision space */
norm = mo_get_norm(problem1->best_parameter, problem2->best_parameter, problem->number_of_variables);
if (norm < apart_enough) {
coco_debug(
"suite_biobj_get_new_instance(): The extremal optimal points of %s are too close in the decision space",
problem->problem_id);
coco_debug("norm = %e", norm);
break_search = 1;
}
}
}
}
/* Clean up */
if (problem) {
coco_problem_stacked_free(problem);
problem = NULL;
}
if (break_search) {
/* The search was broken, continue with next instance2 */
continue;
} else {
/* An appropriate instance was found */
appropriate_instance_found = 1;
coco_info("suite_biobj_set_new_instance(): Instance %lu created from instances %lu and %lu", instance,
instance1, instance2);
/* Save the instance to new_instances */
for (i = 0; i < data->max_new_instances; i++) {
if (data->new_instances[i][0] == 0) {
data->new_instances[i][0] = instance;
data->new_instances[i][1] = instance1;
data->new_instances[i][2] = instance2;
break;
};
}
}
}
if (!appropriate_instance_found) {
coco_error("suite_biobj_get_new_instance(): Could not find suitable instance %lu in $lu tries", instance, num_tries);
return 0; /* Never reached */
}
return instance2;
}
