//Single sigma version
private void evolution(int p_i, string logfile)
{
int[] parent = new int[lambda];
int allpopulationsize = lambda + M;//lambda + M strategy
esvariable[] tmppopulation = new esvariable[allpopulationsize];
//Copy parents to the tmppopulation
if (!allsearch)
{
for (int i = 0; i < M; i++)
{
esvariable prt = new esvariable(size, parents[i].getx(), l, u, wh, numberoforders, computing, routingSolver, mysocketservers);//re-evaluate parents since numberoforders might have changed
tmppopulation[i] = prt;
}
;
}
else
{
for (int i = 0; i < M; i++)
{
tmppopulation[i] = parents[i];
}
;
}
//Create lambda number of children from their parents
for (int i = 0; i < lambda; i++)
{
//bool infeasibledesign = true;
int index = M + i;
parent[i] = Convert.ToInt32(i * M / lambda);//Select each parent in a deterministic way
double[] x = new double[size];
esvariable child = null;
{
//Create this part only p
for (int k = 0; k < l.Count(); k++)
{
x[k] = parents[parent[i]].getx()[k] + normalDist(0, sigma, rnd1[i]); //Single sigma is used
}
setx(x); //Check if they are out of bounds and correct them, this has to be done in here before creating aisles randomly
for (int k = l.Count(); k < size; k++) //It won't enter this loop unless it is new encoding
{
int indexencodingprobability = k - (this.size - l.Count());
if (x[indexencodingprobability] == 1)
{
x[k] = 1;
}
else if (x[indexencodingprobability] == 0)
{
x[k] = 0;
}
else
{
if (uniformrandom(0, 1, rnd1[i]) < x[indexencodingprobability])//If random number is lower than the probability of creating that aisle than instance is 0
{
x[k] = 0;
}
else
{
x[k] = 1;
}
}
}
child = new esvariable(size, x, l, u, wh, numberoforders, computing, routingSolver, mysocketservers);
//writeLog(logfile, child);
}
tmppopulation[index] = child;
if (!usenft)//Do not use Near Feasibility Threshold
{
//If created child is better than parent then increase counter
if (child.getCost() < parents[parent[i]].getCost())
{
numberofsuccessfulchildrencreated++;
numberofsuccessfulchildrencreatedperiteration++;
}
}
else//Use Near Feasibility Threshold
{
//If created child is better than parent then increase counter
if (child.getCost(p_i + 1, nft0, nftlambda) < parents[parent[i]].getCost(p_i + 1, nft0, nftlambda))
{
numberofsuccessfulchildrencreated++;
numberofsuccessfulchildrencreatedperiteration++;
}
}
numberofchildrencreated++;
}
if (!usenft)
{
tmppopulation = sortPopulation(tmppopulation);//Sort lambda + M
}
else
{
tmppopulation = sortPopulation(tmppopulation, p_i);
}
for (int i = 0; i < M; i++)//Select best M individuals as new parents
{
parents[i] = tmppopulation[i];
}
}
/// <summary>
/// Write each solution (each child) to a log file
/// </summary>
/// <param name="p_logfile">Location of the log file</param>
/// <param name="p_esvar">ES solution</param>
private void writeLog(string p_logfile, esvariable p_esvar)
{
csvexport myexcel = new csvexport();
myexcel.addRow();
myexcel["Cost"] = "";
myexcel["Locs"] = "";
myexcel["SR"] = "";
myexcel["Width"] = p_esvar.getWarehouse().getWidth().ToString();
myexcel["Depth"] = p_esvar.getWarehouse().getDepth().ToString();
myexcel["Area"] = p_esvar.getWarehouse().area.ToString();
myexcel["Ratio"] = p_esvar.getx()[0].ToString();
myexcel["E1"] = p_esvar.getx()[1].ToString();
myexcel["E2"] = p_esvar.getx()[2].ToString();
myexcel["E3"] = p_esvar.getx()[3].ToString();
myexcel["E4"] = p_esvar.getx()[4].ToString();
myexcel["I1X"] = p_esvar.getx()[5].ToString();
myexcel["I1Y"] = p_esvar.getx()[6].ToString();
myexcel["PD"] = p_esvar.getx()[7].ToString();
myexcel["Angle1"] = (180 * p_esvar.getx()[8]).ToString();
myexcel["Angle2"] = (180 * p_esvar.getx()[9]).ToString();
myexcel["Angle3"] = (180 * p_esvar.getx()[10]).ToString();
myexcel["Angle4"] = (180 * p_esvar.getx()[11]).ToString();
myexcel["Angle5"] = (180 * p_esvar.getx()[12]).ToString();
myexcel["Angle6"] = (180 * p_esvar.getx()[13]).ToString();
myexcel["Angle7"] = (180 * p_esvar.getx()[14]).ToString();
myexcel["Angle8"] = (180 * p_esvar.getx()[15]).ToString();
myexcel["Adjuster1"] = p_esvar.getx()[16].ToString();
myexcel["Adjuster2"] = p_esvar.getx()[17].ToString();
myexcel["Adjuster3"] = p_esvar.getx()[18].ToString();
myexcel["Adjuster4"] = p_esvar.getx()[19].ToString();
myexcel["Adjuster5"] = p_esvar.getx()[20].ToString();
myexcel["Adjuster6"] = p_esvar.getx()[21].ToString();
myexcel["Adjuster7"] = p_esvar.getx()[22].ToString();
myexcel["Adjuster8"] = p_esvar.getx()[23].ToString();
myexcel["PickAdjuster1"] = p_esvar.getx()[24].ToString();
myexcel["PickAdjuster2"] = p_esvar.getx()[25].ToString();
myexcel["PickAdjuster3"] = p_esvar.getx()[26].ToString();
myexcel["PickAdjuster4"] = p_esvar.getx()[27].ToString();
myexcel["PickAdjuster5"] = p_esvar.getx()[28].ToString();
myexcel["PickAdjuster6"] = p_esvar.getx()[29].ToString();
myexcel["PickAdjuster7"] = p_esvar.getx()[30].ToString();
myexcel["PickAdjuster8"] = p_esvar.getx()[31].ToString();
myexcel["PC12"] = p_esvar.getx()[32].ToString();
myexcel["PC13"] = p_esvar.getx()[33].ToString();
myexcel["PC14"] = p_esvar.getx()[34].ToString();
myexcel["PC15"] = p_esvar.getx()[35].ToString();
myexcel["PC23"] = p_esvar.getx()[36].ToString();
myexcel["PC24"] = p_esvar.getx()[37].ToString();
myexcel["PC25"] = p_esvar.getx()[38].ToString();
myexcel["PC34"] = p_esvar.getx()[39].ToString();
myexcel["PC35"] = p_esvar.getx()[40].ToString();
myexcel["PC45"] = p_esvar.getx()[41].ToString();
myexcel["C12"] = p_esvar.getx()[42].ToString();
myexcel["C13"] = p_esvar.getx()[43].ToString();
myexcel["C14"] = p_esvar.getx()[44].ToString();
myexcel["C15"] = p_esvar.getx()[45].ToString();
myexcel["C23"] = p_esvar.getx()[46].ToString();
myexcel["C24"] = p_esvar.getx()[47].ToString();
myexcel["C25"] = p_esvar.getx()[48].ToString();
myexcel["C34"] = p_esvar.getx()[49].ToString();
myexcel["C35"] = p_esvar.getx()[50].ToString();
myexcel["C45"] = p_esvar.getx()[51].ToString();
myexcel.appendToFile(p_logfile);
}
/// <summary>
/// Creates initial population for ES
/// </summary>
public void createInitialParentPopulation()
{
parents = new esvariable[M];
for (int i = 0; i < this.M; i++)
{
double[] x = new double[this.size];
for (int j = 0; j < l.Count(); j++)
{
if (l[j] != u[j])
{
x[j] = uniformrandom(l[j], u[j], rnd[i]);
}
else
{
x[j] = l[j];
}
}
//This part is for connection instances part of the encoding
for (int j = l.Count(); j < this.size; j++)
{
int indexencodingprobability = j - (this.size - l.Count());
if (x[indexencodingprobability] == 1)
{
x[j] = 1;
}
else if (x[indexencodingprobability] == 0)
{
x[j] = 0;
}
else
{
if (uniformrandom(0, 1, rnd1[i]) < x[indexencodingprobability])//If random number is lower than the probability of creating that aisle than instance is 0
{
x[j] = 0;
}
else
{
x[j] = 1;
}
}
}
numberofgaps = Convert.ToInt32(Math.Ceiling(Convert.ToDouble(iteration) / Convert.ToDouble(this.counter)));
if (allsearch)
{
numberoforders = wh.getOrders().Count();
}
else
{
numberoforders = Convert.ToInt32(1 / Convert.ToDouble(numberofgaps) * wh.getOrders().Count());
}
//string logfile = "log.csv";
esvariable p = new esvariable(size, x, l, u, wh, numberoforders, computing, routingSolver, mysocketservers);
//writeLog(logfile, p);
if (p.isInfeasible() == false)
{
parents[i] = p;//add to population
}
else
{
i--;//Do this iteration again
}
}
if (!usenft)//Without Near Feasibility Threshold
{
parents = sortPopulation(parents);
}
else//With Near Feasibility Threshold
{
parents = sortPopulation(parents, 0);
}
}
