chromosome eStrategy::xover_and_mut(const chromosome* const p1, const chromosome* const p2) const {
p1->validate(), p2->validate();
assert(p1->sigmas.size() == p2->sigmas.size() && p1->sigmas.size() == CONF_DIM);
double prob = frand();
auto sp = p1;
if(prob > 0.5) sp = p2;
chromosome::data_block new_sigmas, new_genes;
if(prob < CONF_PROB_MUT) {
double step = exp(PARAM_TAW_PRIME * normrnd() + CONST_STEP_GLOB);
auto xd = normrnd();
for(size_t index = 0; index < sp->sigmas.size(); index++) {
auto s = sp->sigmas.at(index) * step;
if(s < CONF_RANGE_SIGMA && s > -CONF_RANGE_SIGMA)
s = s / abs(s) * CONF_RANGE_SIGMA;
new_sigmas.push_back(s);
auto g = sp->genes.at(index) + s * xd;
if(g > CONF_BOUND_UPPER) g = CONF_BOUND_UPPER;
else if(g < CONF_BOUND_LOWER) g = CONF_BOUND_LOWER;
new_genes.push_back(g);
}
assert(new_genes.size() == new_sigmas.size());
} else if((1 - prob) < CONF_PROB_XOVER) {
double rp = frand(), rq = frand();
for(size_t index = 0; index < sp->genes.size(); index++) {
auto g = (p1->genes.at(index) * rp + p2->genes.at(index) * rq);
if(g > CONF_BOUND_UPPER) g = CONF_BOUND_UPPER;
else if(g < CONF_BOUND_LOWER) g = CONF_BOUND_LOWER;
new_genes.push_back(g);
if(frand() < 0.5) new_sigmas.push_back(p1->sigmas.at(index));
else new_sigmas.push_back(p2->sigmas.at(index));
}
}
auto c = chromosome(new_genes, new_sigmas);
if(this->eval(c) < sp->get_fitness())
return c;
return chromosome(*sp);
}
int CGreyModel::gen_rand_solution(int num)
{
// m_curr_population_num += num;
if (m_curr_population_num >= m_max_population_num)
{
return FAIL;
}
DataType temp_a = 0.00;
DataType temp_u = 0.00;
DataType fitness = 0.00;
DataType fitness_min = 100000000.00;
DataType fitness_max = 0.00;
DataType epsinon = 0.000000000001;
// CData fitness_set;
// vector<int> seed_num;
// srand((unsigned)time(NULL)); /*随机数初始化*/
int i;
for (i=0; i<num; i++)
{
// srand((unsigned)time(NULL)); /*随机数初始化*/
temp_a = get_rand_a();
temp_u = get_rand_u();
// cout<<"temp_a="<<temp_a<<endl;
// cout<<"temp_u="<<temp_u<<endl;
// a.push_back(temp_a);
// u.push_back(temp_u);
//
fitness = get_fitness(temp_a, temp_u);
Fitness temp;
temp.fitness = fitness;
temp.a_value = temp_a;
temp.u_value = temp_u;
fitness_set.push_back(temp);
// seed_num.push_back(0);
//记录最小的fitness
if ( fitness_min - fitness >= epsinon )
{
fitness_min = fitness;
}
//记录最大的fitness
if ( fitness - fitness_max >= epsinon )
{
fitness_max = fitness;
}
// cout<<"fitness="<<fitness<<endl;
}
// cout<<"fitness_max="<<fitness_max<<endl;
// cout<<"fitness_min="<<fitness_min<<endl;
DataType coefficient = 0.00;
coefficient = (DataType)(m_max_seed-m_min_seed)/(DataType)(fitness_max-fitness_min);
int seed_num_temp = 0;
for (i=0; i<num; i++)
{
seed_num_temp = (int)(coefficient*(fitness_set[i].fitness - fitness_min)+m_min_seed);
// cout<<"seed_num_temp="<<seed_num_temp<<endl;
if (seed_num_temp > 1)
{
m_curr_population_num += seed_num_temp;
gen_rand_solution(seed_num_temp);
// cout<<"m_curr_population_num="<<m_curr_population_num<<endl;
}
}
return SUCC;
}
