/**
* @brief Returns the problem from the bbob suite that corresponds to the given parameters.
*
* @param suite The COCO suite.
* @param function_idx Index of the function (starting from 0).
* @param dimension_idx Index of the dimension (starting from 0).
* @param instance_idx Index of the instance (starting from 0).
* @return The problem that corresponds to the given parameters.
*/
static coco_problem_t *suite_bbob_get_problem(coco_suite_t *suite,
const size_t function_idx,
const size_t dimension_idx,
const size_t instance_idx) {
coco_problem_t *problem = NULL;
const size_t function = suite->functions[function_idx];
const size_t dimension = suite->dimensions[dimension_idx];
const size_t instance = suite->instances[instance_idx];
problem = coco_get_bbob_problem(function, dimension, instance);
problem->suite_dep_function = function;
problem->suite_dep_instance = instance;
problem->suite_dep_index = coco_suite_encode_problem_index(suite, function_idx, dimension_idx, instance_idx);
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;
}
/**
* @brief Returns the problem from the bbob-biobj suite that corresponds to the given parameters.
*
* Creates the bi-objective problem by constructing it from two single-objective problems from the bbob
* suite. If the invoked instance number is not in suite_biobj_instances, the function uses the following
* formula to construct a new appropriate instance:
*
* problem1_instance = 2 * biobj_instance + 1
*
* problem2_instance = problem1_instance + 1
*
* If needed, problem2_instance is increased (see also the explanation of suite_biobj_get_new_instance).
*
* @param suite The COCO suite.
* @param function_idx Index of the function (starting from 0).
* @param dimension_idx Index of the dimension (starting from 0).
* @param instance_idx Index of the instance (starting from 0).
* @return The problem that corresponds to the given parameters.
*/
static coco_problem_t *suite_biobj_get_problem(coco_suite_t *suite,
const size_t function_idx,
const size_t dimension_idx,
const size_t instance_idx) {
const size_t num_bbob_functions = 10;
/* Functions from the bbob suite that are used to construct the bi-objective problem. */
const size_t bbob_functions[] = { 1, 2, 6, 8, 13, 14, 15, 17, 20, 21 };
coco_problem_t *problem1, *problem2, *problem = NULL;
size_t function1_idx, function2_idx;
size_t instance1 = 0, instance2 = 0;
const size_t function = suite->functions[function_idx];
const size_t dimension = suite->dimensions[dimension_idx];
const size_t instance = suite->instances[instance_idx];
suite_biobj_t *data = (suite_biobj_t *) suite->data;
size_t i, j;
const size_t num_existing_instances = sizeof(suite_biobj_instances) / sizeof(suite_biobj_instances[0]);
int instance_found = 0;
/* A "magic" formula to compute the BBOB function index from the bi-objective function index */
function1_idx = num_bbob_functions
- coco_double_to_size_t(
floor(-0.5 + sqrt(0.25 + 2.0 * (double) (suite->number_of_functions - function_idx - 1)))) - 1;
function2_idx = function_idx - (function1_idx * num_bbob_functions) +
(function1_idx * (function1_idx + 1)) / 2;
/* First search for instance in suite_biobj_instances */
for (i = 0; i < num_existing_instances; i++) {
if (suite_biobj_instances[i][0] == instance) {
/* The instance has been found in suite_biobj_instances */
instance1 = suite_biobj_instances[i][1];
instance2 = suite_biobj_instances[i][2];
instance_found = 1;
break;
}
}
if ((!instance_found) && (data)) {
/* Next, search for instance in new_instances */
for (i = 0; i < data->max_new_instances; i++) {
if (data->new_instances[i][0] == 0)
break;
if (data->new_instances[i][0] == instance) {
/* The instance has been found in new_instances */
instance1 = data->new_instances[i][1];
instance2 = data->new_instances[i][2];
instance_found = 1;
break;
}
}
}
if (!instance_found) {
/* Finally, if the instance is not found, create a new one */
if (!data) {
/* Allocate space needed for saving new instances */
data = (suite_biobj_t *) coco_allocate_memory(sizeof(*data));
/* Most often the actual number of new instances will be lower than max_new_instances, because
* some of them are already in suite_biobj_instances. However, in order to avoid iterating over
* suite_biobj_instances, the allocation uses max_new_instances. */
data->max_new_instances = suite->number_of_instances;
data->new_instances = (size_t **) coco_allocate_memory(data->max_new_instances * sizeof(size_t *));
for (i = 0; i < data->max_new_instances; i++) {
data->new_instances[i] = (size_t *) malloc(3 * sizeof(size_t));
for (j = 0; j < 3; j++) {
data->new_instances[i][j] = 0;
}
}
suite->data_free_function = suite_biobj_free;
suite->data = data;
}
/* A simple formula to set the first instance */
instance1 = 2 * instance + 1;
instance2 = suite_biobj_get_new_instance(suite, instance, instance1, num_bbob_functions, bbob_functions);
}
problem1 = coco_get_bbob_problem(bbob_functions[function1_idx], dimension, instance1);
problem2 = coco_get_bbob_problem(bbob_functions[function2_idx], dimension, instance2);
problem = coco_problem_stacked_allocate(problem1, problem2);
problem->suite_dep_function = function;
problem->suite_dep_instance = instance;
problem->suite_dep_index = coco_suite_encode_problem_index(suite, function_idx, dimension_idx, instance_idx);
/* Use the standard stacked problem_id as problem_name and construct a new suite-specific problem_id */
coco_problem_set_name(problem, problem->problem_id);
coco_problem_set_id(problem, "bbob-biobj_f%02d_i%02ld_d%02d", function, instance, dimension);
/* Construct problem type */
coco_problem_set_type(problem, "%s_%s", problem1->problem_type, problem2->problem_type);
return problem;
}
