private static void Algorithm3()
{
List <Chromosome> generation;
Stopwatch sw;
TimeSpan ts3;
generation = Chromosome.Copy(initialChromosome);
//算法GA
//开始计时
sw = new Stopwatch();
sw.Start();
double lastMin = 0;
for (int i = 0, noIm = 0; i < G; i++)
{
Console.WriteLine("代数：" + i);
generation.Sort(); //从小到大排列染色体
// for (int jj = 0; jj < N; jj++)
// {
// Console.WriteLine("No."+jj+":"+ string.Join(",", generation[jj].encoded));
// Console.WriteLine("No."+jj+":"+ string.Join(",",generation[jj].GetDecoded()));
// }
//Console.WriteLine("方法GA最优染色体编码为: " + string.Join(",", generation.First().encoded));
// Console.WriteLine("方法GA最优适应函数值为: " + lastMin);
//Console.WriteLine("代数1：" + i);
if (i != 0) //至少执行一次
{
if (lastMin - generation.First().GetFitness() <= NoImprovedCriticalCondition) //如果没有改进,计数加一
{
noIm++;
}
else
{
Console.WriteLine("未改进代数：" + noIm);
noIm = 0;
}
}
if (noIm >= MaxGenerationNoImproved)//
{
Console.WriteLine("代数：" + i);
break;
}
lastMin = generation.First().GetFitness();//更新 lastmin
//generation.Sort(); //从小到大排列染色体
var elite = generation.GetRange(0, N);
//精英 进行 选择 ,交叉,变异
elite = Chromosome.Select(elite);
//Console.WriteLine("代数2：" + i);
Chromosome.Crossover(elite);
//Console.WriteLine("代数3：" + i);
Chromosome.Mutation(elite);
//Console.WriteLine("代数4：" + i);
generation.Clear();
generation.AddRange(elite);
//Chromosome.Intensify(generation.GetRange(N / 2, N / 2), generation.Min());
}
generation.Sort();
// Console.WriteLine("方法GA最优染色体编码为: " + string.Join(",", generation.First().encoded));
Console.WriteLine("方法GA最优染色体序列为: " + string.Join(",", generation.First().GetDecoded()));
Console.WriteLine("方法GA最优适应函数值为: " + generation.First().GetFitness());
Console.WriteLine("最优适应函数值为: " + HistoryRecords.Values.Min());
//结束计时
sw.Stop();
ts3 = sw.Elapsed;
Console.WriteLine("方法GA总共花费{0}ms.", ts3.TotalMilliseconds);
Console.WriteLine(" ");
}
private static void Algorithm1()
{
//算法一
//首先复制初始染色体,注意,因为list是一个引用类型,不能简单的赋值,又因为lsit中存放的也是引用类型,也不能使用浅克隆.
var generation = Chromosome.Copy(initialChromosome);
//开始计时
Stopwatch sw = new Stopwatch();
sw.Start();
double lastMin = 0;
for (int i = 0, noIm = 0; i < G; i++)
{
generation.Sort(); //从小到大排列染色体
if (i != 0) //至少执行一次
{
if (lastMin - generation.First().GetFitness() <= NoImprovedCriticalCondition) //如果没有改进,计数加一
{
noIm++;
}
else
{
Console.WriteLine("未改进代数：" + noIm);
noIm = 0;
}
}
if (noIm >= MaxGenerationNoImproved)//
{
Console.WriteLine("代数：" + i);
break;
}
lastMin = generation.First().GetFitness();//更新 lastmain
//舍弃适应度函数值较大的后2/1染色体
generation.RemoveRange(N / 2, N / 2);
//强化前二分之一
Chromosome.Intensify(generation, generation.Min());
var elite = Chromosome.Copy(generation);
//选择操作
elite = Chromosome.Select(elite);
//交叉
Chromosome.Crossover1(elite);
//变异
Chromosome.Mutation(elite);
generation.AddRange(elite);
}
generation.Sort();
//Console.WriteLine("方法1最优染色体编码为: " + string.Join(",", generation.First().encoded));
Console.WriteLine("方法1最优染色体序列为: " + string.Join(",", generation.First().GetDecoded()));
Console.WriteLine("方法1最优适应函数值为: " + generation.First().GetFitness());
Console.WriteLine("最优适应函数值为: " + HistoryRecords.Values.Min());
//结束计时
sw.Stop();
TimeSpan ts2 = sw.Elapsed;
Console.WriteLine("方法1总共花费{0}ms.", ts2.TotalMilliseconds);
Console.WriteLine(" ");
}
private static void Algorithm2()
{
List <Chromosome> generation;
Stopwatch sw;
TimeSpan ts2;
generation = Chromosome.Copy(initialChromosome);
//算法二
//开始计时
sw = new Stopwatch();
sw.Start();
double lastMin = 0;
for (int i = 0, noIm = 0; i < G; i++)
{
generation.Sort(); //从小到大排列染色体
// Console.WriteLine("方法2最优染色体编码为: " + string.Join(",", generation.First().encoded));
// Console.WriteLine("方法2最优适应函数值为: " + generation.First().GetFitness());
// Console.WriteLine("方法2最差适应函数值为: " + generation.Last().GetFitness());
if (i != 0) //至少执行一次
{
if (lastMin - generation.First().GetFitness() <= NoImprovedCriticalCondition) //如果没有改进,计数加一
{
noIm++;
}
else
{
noIm = 0;
}
}
//Console.WriteLine("未改进代数：" + noIm);
if (noIm >= MaxGenerationNoImproved)//
{
Console.WriteLine("代数：" + i);
break;
}
lastMin = generation.First().GetFitness();//更新 lastmain
var elite = generation.GetRange(0, N / 2);
var ordinary = generation.GetRange(N / 2, N / 2);
//Console.WriteLine("代数：" + i);
//Console.WriteLine("目前代最优适应函数值为: " + generation.Min().GetFitness());
//Console.WriteLine("目前代第一适应函数值为: " + generation.First().GetFitness());
//Console.WriteLine("目前代最差适应函数值为: " + generation.Last().GetFitness());
//Console.WriteLine("方法2最优染色体序列为: " + string.Join(",", generation.Last().GetDecoded()));
//Console.WriteLine("目前精英最优适应函数值为: " + elite.Min().GetFitness());
//精英 进行 选择 ,交叉,变异
elite = Chromosome.Select(elite);
// Console.WriteLine("目前代最优适应函数值为: " + generation.Min().GetFitness());
//Console.WriteLine("目前精英最优适应函数值为: " + elite.Min().GetFitness());
Chromosome.Crossover(elite);
// Console.WriteLine("目前代最优适应函数值为: " + generation.Min().GetFitness());
// Console.WriteLine("目前精英最优适应函数值为: " + elite.Min().GetFitness());
Chromosome.Mutation(elite);
//Console.WriteLine("目前代最优适应函数值为: " + generation.Min().GetFitness());
//Console.WriteLine("目前精英最优适应函数值为: " + elite.Min().GetFitness());
Chromosome.Intensify(ordinary, elite.Min());
generation.Clear();
generation.AddRange(elite);
generation.AddRange(ordinary);
// Console.WriteLine("目前代最优适应函数值为: " + generation.Min().GetFitness());
//Console.WriteLine("目前精英最优适应函数值为: " + elite.Min().GetFitness());
//Console.WriteLine(" ");
}
generation.Sort();
// Console.WriteLine("方法2最优染色体编码为: " + string.Join(",", generation.First().encoded));
Console.WriteLine("方法2最优染色体序列为: " + string.Join(",", generation.First().GetDecoded()));
Console.WriteLine("方法2最优适应函数值为: " + generation.First().GetFitness());
Console.WriteLine("最优适应函数值为: " + HistoryRecords.Values.Min());
//结束计时
sw.Stop();
ts2 = sw.Elapsed;
Console.WriteLine("方法2总共花费{0}ms.", ts2.TotalMilliseconds);
Console.WriteLine(" ");
}
