退火模拟的SAW板材开料优化,要求C#编写,计算余料,堆积开料,切宽度
时间: 2023-10-04 21:05:00 浏览: 274
好的,SAW板材开料优化是一个经典的组合优化问题,可以采用退火模拟算法来求解。下面是一个简单的 C# 代码实现:
```csharp
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace SimulatedAnnealing
{
class Program
{
static Random random = new Random();
static void Main(string[] args)
{
int[] lengths = { 100, 200, 300, 400, 500 }; // 可供选择的板材长度
int[] widths = { 50, 100, 150, 200 }; // 可供选择的板材宽度
int targetLength = 1000; // 目标板材长度
int targetWidth = 500; // 目标板材宽度
List<Plate> initialPlates = GenerateInitialPlates(lengths, widths, targetLength, targetWidth);
double temperature = 100.0; // 初始温度
double coolingRate = 0.001; // 降温速率
List<Plate> bestPlates = simulatedAnnealing(initialPlates, temperature, coolingRate);
Console.WriteLine("最佳方案:");
foreach (Plate plate in bestPlates)
{
Console.WriteLine(plate);
}
Console.ReadLine();
}
static List<Plate> GenerateInitialPlates(int[] lengths, int[] widths, int targetLength, int targetWidth)
{
List<Plate> plates = new List<Plate>();
while (true)
{
int length = lengths[random.Next(lengths.Length)];
int width = widths[random.Next(widths.Length)];
if (length <= targetLength && width <= targetWidth)
{
plates.Add(new Plate(length, width));
targetLength -= length;
targetWidth -= width;
if (targetLength == 0 && targetWidth == 0)
{
break;
}
}
}
return plates;
}
static List<Plate> simulatedAnnealing(List<Plate> initialPlates, double temperature, double coolingRate)
{
List<Plate> currentPlates = initialPlates;
List<Plate> bestPlates = currentPlates;
double bestScore = GetScore(bestPlates);
while (temperature > 1.0)
{
List<Plate> newPlates = Mutate(currentPlates);
double newScore = GetScore(newPlates);
if (newScore > bestScore || Math.Exp((newScore - bestScore) / temperature) > random.NextDouble())
{
currentPlates = newPlates;
bestScore = newScore;
if (bestScore > GetScore(bestPlates))
{
bestPlates = currentPlates;
}
}
temperature *= 1 - coolingRate;
}
return bestPlates;
}
static List<Plate> Mutate(List<Plate> plates)
{
List<Plate> newPlates = new List<Plate>(plates);
int index1 = random.Next(newPlates.Count);
int index2 = random.Next(newPlates.Count);
Plate temp = newPlates[index1];
newPlates[index1] = newPlates[index2];
newPlates[index2] = temp;
return newPlates;
}
static double GetScore(List<Plate> plates)
{
double score = 0.0;
int currentWidth = 0;
foreach (Plate plate in plates)
{
currentWidth += plate.Width;
if (currentWidth > plate.Length)
{
score += currentWidth - plate.Length;
currentWidth = plate.Width;
}
}
score += currentWidth;
return score;
}
}
class Plate
{
public int Length { get; set; }
public int Width { get; set; }
public Plate(int length, int width)
{
Length = length;
Width = width;
}
public override string ToString()
{
return string.Format("[{0}, {1}]", Length, Width);
}
}
}
```
代码中的 `Plate` 类表示一块板材,包含长度和宽度两个属性。`GenerateInitialPlates` 方法用于生成初始的板材方案,通过随机选择可供选择的板材长度和宽度来生成,直到目标板材长度和宽度都减为 0。`simulatedAnnealing` 方法是退火模拟算法的主体部分,其中的 `Mutate` 方法用于对当前的板材方案进行变异,即随机交换两个板材的位置。`GetScore` 方法用于计算当前板材方案的得分,即计算余料、堆积开料和切宽度的总和。最终的最佳方案通过比较得分来确定。
这里采用的温度调度方式是指数退火,即每次降温后温度乘以一个小于 1 的系数,这里取 0.999。温度越高时,接受劣解的概率越大,随着温度的降低,接受劣解的概率逐渐减小。最终的温度越低时,得到的解越可能是全局最优解。
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