/**
* Function called when GET operation on stats is done.
*/
static void
get_done (void *cls, int success)
{
struct StatMaster *sm = cls;
GNUNET_break (GNUNET_OK == success);
sm->value++;
stat_run (sm, sm->op, sm->stat, NULL);
}
int jde_alg::run()
{
if ( !m_ppara )
return -1;
timer elapsed_t;
// retrieve algorithm parameters
size_t pop_size=m_ppara->get_pop_size();
size_t num_dims=m_ppara->get_dim();
double vtr=m_ppara->get_vtr();
double f_low_bnd,f_up_bnd;
f_low_bnd=m_ppara->get_f_low_bnd();
f_up_bnd=m_ppara->get_f_up_bnd();
double tau_1,tau_2;
tau_1=m_ppara->get_tau_1();
tau_2=m_ppara->get_tau_2();
int m_cur_run;
int max_run=m_ppara->get_max_run();// run/trial number
// shared_ptr<progress_display> pprog_dis;// algorithm progress indicator from boost
// alloc_prog_indicator(pprog_dis);
// allocate original pop and trial pop
population pop(pop_size);
allocate_pop(pop,num_dims,stra_num);
population trial_pop(pop_size);
allocate_pop(trial_pop,num_dims,stra_num);
// generate algorithm statistics output file name
ofstream stat_file(m_com_out_path.stat_path.c_str());
// allocate stop condition object dynamically
alloc_stop_cond();
size_t shuffle_size=pop_size-1;
vector<int> vec_idx1(shuffle_size);
// random U(0,1) generator
uniform_01<> dist_01;
variate_generator<mt19937&, uniform_01<> > rnd_01(gen, dist_01);
// generator for random DIMENSION index
uniform_int<> dist_dim(0,num_dims-1);
variate_generator<mt19937&, uniform_int<> > rnd_dim_idx(gen, dist_dim);
// iteration start
for ( m_cur_run=0;m_cur_run<max_run;m_cur_run++ )
{
reset_run_stat();
m_de_stat.reset();
// jde SPECIFIC,initialize F value vector EVERY single run
size_t i;
for ( i=0;i<pop_size;i++ )
{
pop[i].stra[pr].assign(1,0.0);
pop[i].stra[f].assign(1,0.0);
}
set_orig_pop(pop);
update_diversity(pop);
calc_de_para_stat(pop);
record_de_para_stat(m_cur_run);
record_gen_vals(m_alg_stat,m_cur_run);
print_run_times(stat_file,m_cur_run+1);
print_run_title(stat_file);
// output original population statistics
print_gen_stat(stat_file,1,m_alg_stat);
m_cur_gen=1;
while ( false==(*m_pstop_cond) ) // for every iteration
{
m_de_stat.reset();
int rnd_dim;
double f_i;
double pr_i;
double dim_mut_chance;
size_t i,j,k;
trial_pop=pop;// operator =
for ( i=0;i<pop_size;i++ )
{
// generating three mutually different individual index other than i using random shuffle
// initialize index vector
for ( k=0;k<shuffle_size;k++ )
{
if ( k<i )
vec_idx1[k]=k;
else
// EXCLUDE i
vec_idx1[k]=(k+1)%pop_size;
}
// random shuffle
for ( k=0;k<shuffle_size;k++ )
{
// generator for random SHUFFLE VECTOR index
uniform_int<> dist_uni_shuf(k,shuffle_size-1);
variate_generator<mt19937&, uniform_int<> > rnd_shuf_idx(gen, dist_uni_shuf);
int idx_tmp=rnd_shuf_idx();
swap(vec_idx1[k],vec_idx1[idx_tmp]);
}
int i1,i2,i3;// i!=i1!=i2!=i3
i1=vec_idx1[0];
i2=vec_idx1[1];
i3=vec_idx1[2];
rnd_dim=rnd_dim_idx();
double pr_chance=rnd_01();
if ( pr_chance<=tau_2 )
{
pr_i=rnd_01();
trial_pop[i].stra[pr][0]=pr_i;
}
else
pr_i=trial_pop[i].stra[pr][0];
// scaling factor F self-adaptive update equation
double f_chance=rnd_01();
if ( f_chance<=tau_1 )
{
f_i=f_low_bnd+rnd_01()*f_up_bnd;
trial_pop[i].stra[f][0]=f_i;
}
else
f_i=trial_pop[i].stra[f][0];// keep unchanged at thsi iteration
for ( j=0;j<num_dims;j++ )
{
dim_mut_chance=rnd_01();
if ( rnd_dim==j || dim_mut_chance<=pr_i )
{
trial_pop[i].x[j]=trial_pop[i1].x[j]+f_i*(trial_pop[i2].x[j]-trial_pop[i3].x[j]);
// boundaries check
bound_check(trial_pop[i].x[j],j);
}
}// for every dimension
}// for every particle
// evaluate pop
eval_pop(trial_pop,*m_pfunc,m_alg_stat);
update_pop(pop,trial_pop);
stat_pop(pop,m_alg_stat);
update_search_radius();
update_diversity(pop);
calc_de_para_stat(pop);
record_de_para_stat(m_cur_run);
record_gen_vals(m_alg_stat,m_cur_run);
print_gen_stat(stat_file,m_cur_gen+1,m_alg_stat);
update_conv_stat(vtr);
/*if ( run_once )
++(*pprog_dis);*/
m_cur_gen++;
}// while single run stop_condition is false
// single run end
stat_run(pop,m_cur_run);// stat single run for algorithm analysis
if ( is_final_run(m_cur_run,max_run) )
print_run_stat(stat_file,m_alg_stat,max_run);
/*if ( !run_once )
++(*pprog_dis);*/
}// for every run
print_avg_gen(stat_file,m_alg_stat.run_avg_gen);
// stat and output average time per run by second
m_alg_stat.run_avg_time=elapsed_t.elapsed();
m_alg_stat.run_avg_time /= (max_run*1.0);
print_avg_time(stat_file,m_alg_stat.run_avg_time);
print_best_x(stat_file,m_alg_stat.bst_ind);
write_stat_vals();
cout<<endl;// flush cout output
return 0;
}// end function Run
int dgea_alg::run()
{
timer elapsed_t;
// retrieve algorithm parameters
size_t pop_size=m_ppara->get_pop_size();
size_t num_dims=m_ppara->get_dim();
double vtr=m_ppara->get_vtr();
double d_h=m_ppara->get_dh();
double d_l=m_ppara->get_dh();
int m_cur_run;
int max_run=m_ppara->get_max_run();
// run/trial number
//shared_ptr<progress_display> pprog_dis;
//// algorithm progress indicator from boost
//alloc_prog_indicator(pprog_dis);
// allocate pop
population pop(pop_size);
allocate_pop(pop,num_dims);
population child_pop(pop_size);
allocate_pop(child_pop,num_dims);
// generate algorithm statistics output file name
ofstream stat_file(m_com_out_path.stat_path.c_str());
// alloc stop condition
alloc_stop_cond();
for(m_cur_run=0; m_cur_run<max_run; ++m_cur_run)
{
reset_run_stat();
set_orig_pop(pop);
update_diversity(pop);
print_run_times(stat_file,m_cur_run+1);
print_run_title(stat_file);
// output original population statistics
print_gen_stat(stat_file,1,m_alg_stat);
record_gen_vals(m_alg_stat,m_cur_run);
m_cur_gen=1;
int mode=exploit;
while ( false==(*m_pstop_cond) ) // for every iteration
{
update_mode(mode,d_l,d_h);
gen_child(mode,pop,child_pop);
eval_pop(child_pop, *m_pfunc, m_alg_stat);
select(pop,child_pop);
stat_pop(pop, m_alg_stat);
update_search_radius();
update_diversity(pop);
print_gen_stat(stat_file,m_cur_gen+1,m_alg_stat);
record_gen_vals(m_alg_stat,m_cur_run);
update_conv_stat(vtr);
m_cur_gen++;
}// while single run termination criterion is not met
// single run end
stat_run(pop,m_cur_run);// stat single run for algorithm analysis
if ( is_final_run(m_cur_run,max_run) )
print_run_stat(stat_file,m_alg_stat,max_run);
/*if ( !run_once )
++(*pprog_dis); */
}
print_avg_gen(stat_file,m_alg_stat.run_avg_gen);
// stat and output average time per run by second
m_alg_stat.run_avg_time=elapsed_t.elapsed();
m_alg_stat.run_avg_time /= (max_run*1.0);
print_avg_time(stat_file,m_alg_stat.run_avg_time);
print_best_x(stat_file,m_alg_stat.bst_ind);
write_stat_vals();
cout<<endl;// flush cout output
return 0;
}// end function Run
