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refactor: reduce scope of some variables #631

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3 changes: 1 addition & 2 deletions src/algorithms/compass_search.cpp
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Expand Up @@ -115,13 +115,12 @@ population compass_search::evolve(population pop) const
vector_double cur_best_x(std::move(sel_xf.first)), cur_best_f(std::move(sel_xf.second));

// We need some auxiliary variables
bool flag = false;
unsigned fevals = 0u;

double newrange = m_start_range;

while (newrange > m_stop_range && fevals <= m_max_fevals) {
flag = false;
bool flag = false;
for (decltype(dim) i = 0u; i < dim; i++) {
auto x_trial = cur_best_x;
// move up
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3 changes: 1 addition & 2 deletions src/algorithms/ihs.cpp
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Expand Up @@ -120,7 +120,6 @@ population ihs::evolve(population pop) const

// Declarations
vector_double new_x(dim, 0.);
std::vector<vector_double::size_type> best_idxs(pop.size());

// Main loop
for (decltype(m_gen) gen = 1u; gen <= m_gen; ++gen) {
Expand Down Expand Up @@ -185,7 +184,7 @@ population ihs::evolve(population pop) const
// we augment the list with the new fitness
fitnesses.push_back(new_f);
// select the best pop.size() individuals
best_idxs = select_best_N_mo(fitnesses, pop.size());
const std::vector<vector_double::size_type> best_idxs = select_best_N_mo(fitnesses, pop.size());
// define the new population
for (population::size_type i = 0u; i < pop.size(); ++i) {
if (best_idxs[i] == pop.size()) { // this is the new guy
Expand Down
4 changes: 2 additions & 2 deletions src/algorithms/nsga2.cpp
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Expand Up @@ -132,7 +132,7 @@ population nsga2::evolve(population pop) const
m_log.clear();

// Declarations
std::vector<vector_double::size_type> best_idx(NP), shuffle1(NP), shuffle2(NP);
std::vector<vector_double::size_type> shuffle1(NP), shuffle2(NP);
vector_double::size_type parent1_idx, parent2_idx;
std::pair<vector_double, vector_double> children;

Expand Down Expand Up @@ -296,7 +296,7 @@ population nsga2::evolve(population pop) const
}
// This method returns the sorted N best individuals in the population according to the crowded comparison
// operator
best_idx = select_best_N_mo(popnew.get_f(), NP);
const std::vector<vector_double::size_type> best_idx = select_best_N_mo(popnew.get_f(), NP);
// We insert into the population
for (population::size_type i = 0; i < NP; ++i) {
pop.set_xf(i, popnew.get_x()[best_idx[i]], popnew.get_f()[best_idx[i]]);
Expand Down
19 changes: 8 additions & 11 deletions src/algorithms/pso.cpp
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Expand Up @@ -218,9 +218,6 @@ population pso::evolve(population pop) const
double acceleration_coefficient = m_eta1 + m_eta2;
double sum_forces;

double r1 = 0.;
double r2 = 0.;

/* --- Main PSO loop ---
*/
// For each generation
Expand All @@ -240,8 +237,8 @@ population pso::evolve(population pop) const
/*-------Original algorithm used in the first PaGMO paper (~2007) ------------------------*/
if (m_variant == 1u) {
for (decltype(dim) d = 0u; d < dim; ++d) {
r1 = drng(m_e);
r2 = drng(m_e);
const double r1 = drng(m_e);
const double r2 = drng(m_e);
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r2 * (best_neighb[d] - X[p][d]);
}
Expand All @@ -252,7 +249,7 @@ population pso::evolve(population pop) const
/*-------Check with Rastrigin-------------------------------------------------------------*/
else if (m_variant == 2u) {
for (decltype(dim) d = 0u; d < dim; ++d) {
r1 = drng(m_e);
const double r1 = drng(m_e);
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r1 * (best_neighb[d] - X[p][d]);
}
Expand All @@ -261,8 +258,8 @@ population pso::evolve(population pop) const
/*-------PSO variant (commonly mistaken in literature for the canonical)----------------*/
/*-------Same random number for all components------------------------------------------*/
else if (m_variant == 3u) {
r1 = drng(m_e);
r2 = drng(m_e);
const double r1 = drng(m_e);
const double r2 = drng(m_e);
for (decltype(dim) d = 0u; d < dim; ++d) {
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r2 * (best_neighb[d] - X[p][d]);
Expand All @@ -273,7 +270,7 @@ population pso::evolve(population pop) const
/*-------Same random number for all components------------------------------------------*/
/*-------and with equal random weights of social and cognitive components---------------*/
else if (m_variant == 4u) {
r1 = drng(m_e);
const double r1 = drng(m_e);
for (decltype(dim) d = 0u; d < dim; ++d) {
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r1 * (best_neighb[d] - X[p][d]);
Expand All @@ -295,8 +292,8 @@ population pso::evolve(population pop) const
*-------------------------------------------------------------------------------------*/
else if (m_variant == 5u) {
for (decltype(dim) d = 0u; d < dim; ++d) {
r1 = drng(m_e);
r2 = drng(m_e);
const double r1 = drng(m_e);
const double r2 = drng(m_e);
m_V[p][d] = m_omega
* (m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r2 * (best_neighb[d] - X[p][d]));
Expand Down
19 changes: 8 additions & 11 deletions src/algorithms/pso_gen.cpp
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Expand Up @@ -220,9 +220,6 @@ population pso_gen::evolve(population pop) const
double acceleration_coefficient = m_eta1 + m_eta2;
double sum_forces;

double r1 = 0.;
double r2 = 0.;

/* --- Main PSO loop ---
*/
// For each generation
Expand All @@ -242,8 +239,8 @@ population pso_gen::evolve(population pop) const
/*-------Original algorithm used in the first PaGMO paper (~2007) ------------------------*/
if (m_variant == 1u) {
for (decltype(dim) d = 0u; d < dim; ++d) {
r1 = drng(m_e);
r2 = drng(m_e);
const double r1 = drng(m_e);
const double r2 = drng(m_e);
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r2 * (best_neighb[d] - X[p][d]);
}
Expand All @@ -254,7 +251,7 @@ population pso_gen::evolve(population pop) const
/*-------Check with Rastrigin-------------------------------------------------------------*/
else if (m_variant == 2u) {
for (decltype(dim) d = 0u; d < dim; ++d) {
r1 = drng(m_e);
const double r1 = drng(m_e);
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r1 * (best_neighb[d] - X[p][d]);
}
Expand All @@ -263,8 +260,8 @@ population pso_gen::evolve(population pop) const
/*-------PSO variant (commonly mistaken in literature for the canonical)----------------*/
/*-------Same random number for all components------------------------------------------*/
else if (m_variant == 3u) {
r1 = drng(m_e);
r2 = drng(m_e);
const double r1 = drng(m_e);
const double r2 = drng(m_e);
for (decltype(dim) d = 0u; d < dim; ++d) {
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r2 * (best_neighb[d] - X[p][d]);
Expand All @@ -275,7 +272,7 @@ population pso_gen::evolve(population pop) const
/*-------Same random number for all components------------------------------------------*/
/*-------and with equal random weights of social and cognitive components---------------*/
else if (m_variant == 4u) {
r1 = drng(m_e);
const double r1 = drng(m_e);
for (decltype(dim) d = 0u; d < dim; ++d) {
m_V[p][d] = m_omega * m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r1 * (best_neighb[d] - X[p][d]);
Expand All @@ -297,8 +294,8 @@ population pso_gen::evolve(population pop) const
*-------------------------------------------------------------------------------------*/
else if (m_variant == 5u) {
for (decltype(dim) d = 0u; d < dim; ++d) {
r1 = drng(m_e);
r2 = drng(m_e);
const double r1 = drng(m_e);
const double r2 = drng(m_e);
m_V[p][d] = m_omega
* (m_V[p][d] + m_eta1 * r1 * (lbX[p][d] - X[p][d])
+ m_eta2 * r2 * (best_neighb[d] - X[p][d]));
Expand Down
6 changes: 3 additions & 3 deletions src/algorithms/simulated_annealing.cpp
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Expand Up @@ -145,7 +145,7 @@ population simulated_annealing::evolve(population pop) const

// Stores the number of accepted points for each component
std::vector<int> acp(dim, 0u);
double ratio = 0., currentT = m_Ts, probab = 0.;
double currentT = m_Ts;

// Main SA loops
for (decltype(m_n_T_adj) jter = 0u; jter < m_n_T_adj; ++jter) {
Expand Down Expand Up @@ -174,7 +174,7 @@ population simulated_annealing::evolve(population pop) const
}
} else {
// test it with Boltzmann to decide the acceptance
probab = std::exp(-std::abs(fOLD[0] - fNEW[0]) / currentT);
const double probab = std::exp(-std::abs(fOLD[0] - fNEW[0]) / currentT);
// we compare prob with a random probability.
if (probab > drng(m_e)) {
xOLD[nter] = xNEW[nter];
Expand Down Expand Up @@ -209,7 +209,7 @@ population simulated_annealing::evolve(population pop) const
} // end for(kter = 0; ...
// adjust the step (adaptively)
for (decltype(dim) iter = 0u; iter < dim; ++iter) {
ratio = static_cast<double>(acp[iter]) / static_cast<double>(m_bin_size);
const double ratio = static_cast<double>(acp[iter]) / static_cast<double>(m_bin_size);
acp[iter] = 0u; // reset the counter
if (ratio > .6) {
// too many acceptances, increase the step by a factor 3 maximum
Expand Down
7 changes: 2 additions & 5 deletions src/problems/cec2006.cpp
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Expand Up @@ -918,9 +918,6 @@ void cec2006::g19_objfun_impl(vector_double &f, const vector_double &x) const
/// Implementation of the constraint function.
void cec2006::g19_compute_constraints_impl(vector_double &c, const vector_double &x) const
{
double sum1 = 0.;
double sum2 = 0.;

double A[10][5] = {{-16.0, 2.0, 0.0, 1.0, 0.0}, {0.0, -2.0, 0.0, 0.4, 2.0}, {-3.5, 0.0, 2.0, 0.0, 0.0},
{0.0, -2.0, 0.0, -4.0, -1.0}, {0.0, -9.0, -2.0, 1.0, -2.8}, {2.0, 0.0, -4.0, 0.0, 0.0},
{-1.0, -1.0, -1.0, -1.0, -1.0}, {-1.0, -2.0, -3.0, -2.0, -1.0}, {1.0, 2.0, 3.0, 4.0, 5.0},
Expand All @@ -937,10 +934,10 @@ void cec2006::g19_compute_constraints_impl(vector_double &c, const vector_double

/* constraints g <= 0 */
for (unsigned j = 0u; j < 5u; ++j) {
sum1 = 0.0;
double sum1 = 0.0;
for (unsigned i = 0u; i < 5u; ++i)
sum1 += C[i][j] * x[10 + i];
sum2 = 0.0;
double sum2 = 0.0;
for (unsigned i = 0u; i < 10u; ++i)
sum2 += A[i][j] * x[i];
c[j] = -((2.0 * sum1) + (3.0 * D[j] * std::pow(x[10 + j], 2.0)) + E[j] - sum2);
Expand Down
4 changes: 2 additions & 2 deletions src/problems/cec2013.cpp
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Expand Up @@ -502,7 +502,7 @@ void cec2013::weierstrass_func(const double *x, double *f, const unsigned nx, co
int r_flag) const /* Weierstrass's */
{
unsigned i, j, k_max;
double sum = 0, sum2 = 0, a, b;
double sum2 = 0, a, b;

shiftfunc(x, &m_y[0], nx, Os);
for (i = 0u; i < nx; ++i) // shrink to the original search range
Expand All @@ -529,7 +529,7 @@ void cec2013::weierstrass_func(const double *x, double *f, const unsigned nx, co
k_max = 20;
f[0] = 0.0;
for (i = 0u; i < nx; ++i) {
sum = 0.0;
double sum = 0.0;
sum2 = 0.0;
for (j = 0u; j <= k_max; ++j) {
sum += std::pow(a, j) * std::cos(2.0 * detail::pi() * std::pow(b, j) * (m_y[i] + 0.5));
Expand Down
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