遗传算法的SAW板材开料优化,要求C#编写,计算余料,堆积开料,切割宽度,切割顺序,切割路径,转向次数
时间: 2023-10-10 16:09:18 浏览: 147
仁霸板材开料切割排版优化软件 v5.2 企业版.zip
遗传算法是一种常用的优化算法,可以应用于板材开料问题。以下是一个简单的SAW板材开料优化的C#实现。
首先定义一个板材类,包含板材的长度、宽度和剩余面积等信息:
```csharp
class Plate
{
public int Length { get; set; }
public int Width { get; set; }
public int Area { get; set; }
public List<Cut> Cuts { get; set; }
public Plate(int length, int width)
{
Length = length;
Width = width;
Area = length * width;
Cuts = new List<Cut>();
}
}
```
接着定义一个切割类,包含切割的长度、宽度和位置等信息:
```csharp
class Cut
{
public int Length { get; set; }
public int Width { get; set; }
public int X { get; set; }
public int Y { get; set; }
public Cut(int length, int width, int x, int y)
{
Length = length;
Width = width;
X = x;
Y = y;
}
}
```
然后定义一个遗传算法类,包含种群、交叉率、变异率等参数:
```csharp
class GeneticAlgorithm
{
private List<Plate> population;
private float crossoverRate = 0.8f;
private float mutationRate = 0.05f;
public GeneticAlgorithm(int populationSize)
{
population = new List<Plate>();
for (int i = 0; i < populationSize; i++)
{
population.Add(new Plate(4000, 2000)); // 初始化种群,每个个体为一块 4000x2000 的板材
}
}
public void Evolve(int generations)
{
for (int i = 0; i < generations; i++)
{
// 计算适应度
foreach (Plate plate in population)
{
plate.Area = plate.Length * plate.Width;
int usedArea = 0;
for (int j = 0; j < plate.Cuts.Count; j++)
{
Cut cut = plate.Cuts[j];
usedArea += cut.Length * cut.Width;
if (j > 0) // 计算转向次数
{
Cut prevCut = plate.Cuts[j - 1];
if (cut.X == prevCut.X)
{
if (prevCut.Y + prevCut.Width != cut.Y)
{
plate.Area += 100;
}
}
else
{
if (prevCut.X + prevCut.Length != cut.X)
{
plate.Area += 100;
}
}
}
}
plate.Area -= usedArea; // 计算余料
}
// 选择父母
List<Plate> parents = new List<Plate>();
while (parents.Count < population.Count)
{
Plate parent1 = SelectParent();
Plate parent2 = SelectParent();
parents.Add(parent1);
parents.Add(parent2);
}
// 交叉
for (int j = 0; j < parents.Count; j += 2)
{
if (Random.NextDouble() < crossoverRate)
{
CrossOver(parents[j], parents[j + 1]);
}
}
// 变异
foreach (Plate plate in population)
{
if (Random.NextDouble() < mutationRate)
{
Mutate(plate);
}
}
}
}
private Plate SelectParent()
{
// 采用轮盘赌选择父母
float sumFitness = population.Sum(p => p.Area);
float rand = (float)Random.NextDouble() * sumFitness;
float partialSum = 0;
foreach (Plate plate in population)
{
partialSum += plate.Area;
if (partialSum >= rand)
{
return plate;
}
}
return population[population.Count - 1];
}
private void CrossOver(Plate parent1, Plate parent2)
{
// 采用单点交叉
int cutPoint = Random.Next(1, Math.Min(parent1.Cuts.Count - 1, parent2.Cuts.Count - 1));
List<Cut> tempCuts = new List<Cut>(parent1.Cuts.GetRange(0, cutPoint));
parent1.Cuts.RemoveRange(0, cutPoint);
parent1.Cuts.AddRange(parent2.Cuts.GetRange(cutPoint, parent2.Cuts.Count - cutPoint));
parent2.Cuts.RemoveRange(cutPoint, parent2.Cuts.Count - cutPoint);
parent2.Cuts.InsertRange(0, tempCuts);
}
private void Mutate(Plate plate)
{
// 采用插入变异
int cutIndex = Random.Next(0, plate.Cuts.Count);
Cut cut = plate.Cuts[cutIndex];
plate.Cuts.RemoveAt(cutIndex);
int x = Random.Next(0, plate.Length - cut.Length);
int y = Random.Next(0, plate.Width - cut.Width);
Cut newCut = new Cut(cut.Length, cut.Width, x, y);
plate.Cuts.Add(newCut);
}
}
```
最后在主函数中使用遗传算法求解:
```csharp
static void Main(string[] args)
{
// 初始化切割方案
List<Cut> cuts = new List<Cut>();
cuts.Add(new Cut(500, 100, 0, 0));
cuts.Add(new Cut(500, 100, 0, 100));
cuts.Add(new Cut(500, 100, 0, 200));
cuts.Add(new Cut(500, 100, 0, 300));
cuts.Add(new Cut(500, 100, 0, 400));
cuts.Add(new Cut(500, 100, 0, 500));
cuts.Add(new Cut(500, 100, 0, 600));
cuts.Add(new Cut(500, 100, 0, 700));
cuts.Add(new Cut(500, 100, 0, 800));
cuts.Add(new Cut(500, 100, 0, 900));
cuts.Add(new Cut(2000, 500, 500, 0));
cuts.Add(new Cut(2000, 500, 500, 500));
cuts.Add(new Cut(2000, 500, 500, 1000));
// 定义种群大小和迭代次数
int populationSize = 100;
int generations = 100;
// 初始化遗传算法
GeneticAlgorithm ga = new GeneticAlgorithm(populationSize);
// 进化
ga.Evolve(generations);
// 打印最优解
Plate bestPlate = ga.population.OrderBy(p => p.Area).First();
Console.WriteLine("余料:{0}", bestPlate.Area);
Console.WriteLine("切割顺序:");
for (int i = 0; i < bestPlate.Cuts.Count; i++)
{
Console.WriteLine("{0}. ({1}, {2})", i + 1, bestPlate.Cuts[i].X, bestPlate.Cuts[i].Y);
}
}
```
上述代码中,切割方案为一个包含若干个切割的列表,每个切割包含长度、宽度和位置等信息。种群大小和迭代次数可以根据实际情况进行调整。在遗传算法的进化过程中,首先计算每个个体的适应度,然后采用轮盘赌选择父母,进行单点交叉和插入变异,得到新一代个体。最后,选择最优个体作为最终方案。
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