t_battery *get_bat(char *name)
{
t_battery *result;
char *path;
int fd;
result = init_bat();
path = my_printf_str("%s/state", name);
fd = open(path, O_RDONLY);
xfree(path);
init_buffer();
if (fd == -1)
return (get_batfail(result));
if (init_batstate(fd, result) == -1)
return (get_batfail(result));
xclose(fd);
path = my_printf_str("%s/info", name);
fd = open(path, O_RDONLY);
xfree(path);
init_buffer();
if (fd == -1)
return (get_batfail(result));
if (init_batinfo(fd, result) == -1)
return (get_batfail(result));
xclose(fd);
return (result);
}
void setup()
{
glClearColor(0.0, 0.0, 0.0, 1.0);
glMatrixMode(GL_PROJECTION);
glLoadIdentity();
glOrtho(-RADIUS, RADIUS, -RADIUS, RADIUS, -RADIUS, RADIUS);
glEnable(GL_DEPTH_TEST);
glShadeModel(GL_SMOOTH);
init_light(GL_LIGHT0, 0, 1, 1, 1, 1, 1);
init_light(GL_LIGHT1, 1, 0, 1, 1, 1, 1);
init_light(GL_LIGHT2, 1, 1, 0, 1, 1, 1);
init_balls();
init_bat();
}
bat_t algorithm(float A, float r, uint pop_size, uint max_it, fit_t (*fit_fnc)(bat_t &), float q_min=0, float q_max=MAX/10, uint debug=1, std::vector<float> *avg_fit_at_it=NULL, std::vector<float> *best_at_iter=NULL) {
srand(time(NULL)); /*inits randomness*/
bats_t population;
bats_fit_t fit;
std::vector<float> Q(pop_size); /*frequency*/
std::vector<bat_t> v(pop_size); /*bats velocity*/
/*inits bats velocity to zero*/
std::for_each(v.begin(), v.end(), [](bat_t &velocity) { velocity = init_bat(BAT_LENGTH, 0.0, 0.0); });
init_population(population, pop_size, BAT_LENGTH);
init_fit(fit, pop_size);
uint it = 0;
do {
/*one iteration of bat alg.*/
evaluate_population(fit, population, fit_fnc);
bat_t best_bat = utils::return_best_bat(population, fit);
if (debug >=2) {
best_at_iter->push_back(fit_fnc(best_bat));
avg_fit_at_it->push_back(std::accumulate(fit.begin(), fit.end(), 0.0) / fit.size());
}
uint index = 0;
std::for_each(population.begin(), population.end(), [&](bat_t &bat) {
//Q[index] = utils::rand_in<float>(Q_MIN, Q_MAX);
Q[index] = utils::rand_in<float>(0.0, 0.1);
bat_t candidate_v = v[index] + (bat - best_bat)*Q[index];
bat_t candidate_bat = bat + candidate_v;
if (utils::rand_in<float>(0.0,1.0) > r) {
/*random walk in direction of candidate_v scalled down by q_max/100 factor*/
for (int i=0;i<2;i++) {
candidate_v = init_bat(BAT_LENGTH)*(q_max/100);
candidate_bat = best_bat+candidate_v;
}
}
/*evaluate candidate solution, it might be either solution found by appling movement equotions to current solution or by using random walk around best solution*/
float candidate_bat_fit = fit_fnc(candidate_bat);
/*accept candidate solution as current solution if better or if not better accept solution with some small probability*/
if (candidate_bat_fit < fit[index] || utils::rand_in<float>(0.0,1.0) < A) {
bat = candidate_bat;
v[index] = candidate_v;
fit[index] = candidate_bat_fit;
}
index++;
});
if (debug>=1 && it%100==1) {
evaluate_population(fit, population, fit_fnc);
bat_t best = utils::return_best_bat(population, fit);
std::cout << "at iteration: " << it << " pop avg fit: " << std::accumulate(fit.begin(), fit.end(), 0.0) / fit.size() << " best bat fit: " << fit_fnc(best) << std::endl;
}
}while(++it < max_it);
/*find and return best solution*/
evaluate_population(fit, population, fit_fnc);
return utils::return_best_bat(population, fit);
}
void init_population(bats_t &pop, uint population_length, uint bat_length) { for (uint i=0; i< population_length; i++) { pop.push_back( init_bat(bat_length) ); } }
