protected Algorithm(int numberOfChromosomes, int replaceByGeneration, int trackBest, Schedule prototype)
{
// 会被后代替换掉的染色体的数量
this.replaceByGeneration = replaceByGeneration;
// 当前染色体组中最好的染色体的数量，初始化为0
this.currentBestSize = 0;
// 初始化的染色体
this.prototype = prototype;
// 当前代，表是当前是第几代染色体
this.currentBestSize = 0;
// 算法运行的状态
this.state = AlgorithmState.AS_USER_STOPED;
// 算法中至少要有两条染色体
if (numberOfChromosomes < 2)
numberOfChromosomes = 2;
// 跟踪至少一条最好的染色体？
if (trackBest < 1)
trackBest = 1;
if (this.replaceByGeneration < 1)
this.replaceByGeneration = 1;
else if (this.replaceByGeneration > numberOfChromosomes - trackBest)
this.replaceByGeneration = numberOfChromosomes - trackBest;
// _chromosomes与_bestFlags大小一样，它们之间有一对一的映射关系
this.chromosomes.Capacity = numberOfChromosomes;
for (int i = 0; i < this.chromosomes.Capacity; i++)
{
chromosomes.Add(new Schedule());
}
this.bestFlags.Capacity = numberOfChromosomes;
for (int i = 0; i < this.bestFlags.Capacity; i++)
{
this.bestFlags.Add(false);
}
this.bestChromosomes.Capacity = trackBest;
for (int i = 0; i < this.bestChromosomes.Capacity; i++)
{
this.bestChromosomes.Add(-1);
}
}
/// <summary>
/// 基于复制的构造器
/// </summary>
/// <param name="c"></param>
/// <param name="setupOnly"></param>
public Schedule(Schedule c, bool setupOnly)
{
if (!setupOnly) // if里是复制一个已存在的染色体
{
// copy code
slots = c.slots;
classes = c.classes;
// copy flags of class requirements
criteria = c.criteria;
// copy fitness
fitness = c.fitness;
}
else // else里是新建一个染色体
{
// reserve space for time-space slots in chromosomes code
slots.Capacity = DAYS_NUM * DAY_HOURS * ROOM_NUM;
for (int i = 0; i < slots.Capacity; i++)
{
slots.Add(new List<CourseClass>());
}
// reserve space for flags of class requirements
criteria.Capacity = COURSE_CLASS_NUM * CRITERIA_NUM;
for (int i = 0; i < criteria.Capacity; i++)
{
criteria.Add(false);
}
}
// copy parameters
numberOfCrossoverPoints = c.numberOfCrossoverPoints;
mutationSize = c.mutationSize;
crossoverProbability = c.crossoverProbability;
mutationProbability = c.mutationProbability;
}
/// <summary>
/// 随机生成一个全新的染色体
/// </summary>
/// <returns></returns>
public Schedule MakeNewFromPrototype(List<CourseClass> courseList)
{
// number of time-space slots
int size = slots.Capacity;
// make new chromosome, copy chromosome setup
// 根据当前的染色体复制一个新的染色体,因为true为setupOnly的值，所以这里生成的染色体是全新的
Schedule newChromosome = new Schedule(this, true);
// place classes at random position
// 将课程放在一个随机的位置
List<CourseClass> c = courseList;// 获取所有课程，存放在类型为list的c中
foreach (CourseClass it in c)// 利用迭代器，开始遍历c
{
// determine random position of class
int nr = ROOM_NUM;
int dur = it.GetDuration();
int day = RandomNbr.GetRandomNbr() % DAYS_NUM;
int room = RandomNbr.GetRandomNbr() % nr;
int time = RandomNbr.GetRandomNbr() % (DAY_HOURS + 1 - dur);
int pos = day * nr * DAY_HOURS + room * DAY_HOURS + time;
for (int i = dur - 1; i >= 0; i--)
{
newChromosome.slots[pos + i].Add(it);
}
newChromosome.classes.Add(it, pos);
}
newChromosome.CalculateFitness();
return newChromosome;
}
/// <summary>
/// 进行交叉配对以生成新的染色体
/// </summary>
/// <param name="parent2"></param>
/// <returns></returns>
public Schedule Crossover(Schedule parent2)
{
// check probability of crossover operation
if (RandomNbr.GetRandomNbr() % 100 > crossoverProbability)
{
return new Schedule(this, false);
}
// new chromosome object, copy chromosome setup
// n 是个全新的染色体
Schedule n = new Schedule(this, true);
// number of classes
int size = classes.Count;
List<bool> cp = new List<bool>();
cp.Capacity = size;
for (int c = 0; c < cp.Capacity; c++)
{
cp.Add(false);
}
// determine crossover point (randomly)
// 随机指定的交叉位置
for (int i = numberOfCrossoverPoints; i > 0; i--)
{
while (true)
{
int p = RandomNbr.GetRandomNbr() % size;
if (!cp[p])
{
cp[p] = true;
break;
}
}
}
// 分别获取需要进行交叉的两个课表的迭代器的首位
IDictionaryEnumerator it1 = this.classes.GetEnumerator();
IDictionaryEnumerator it2 = parent2.classes.GetEnumerator();
bool first = RandomNbr.GetRandomNbr() % 2 == 0;
for (int i = 0; i < size; i++)
{
it1.MoveNext();
it2.MoveNext();
// crossover point
if (cp[i])
{
// change source chromosome
first = !first;
}
if (first)
{
// insert class from first parent into new chromosome's class table
if (n.classes.ContainsKey(it1.Key as CourseClass))
{
n.classes.Remove(it1.Key as CourseClass);
n.classes.Add(it1.Key as CourseClass, (int)it1.Value);
}
else
{
n.classes.Add(it1.Key as CourseClass, (int)it1.Value);
}
// all time-space slots of class are copied
for (int j = (it1.Key as CourseClass).GetDuration() - 1; j >= 0; j--)
{
n.slots[(int)it1.Value + j].Add(it1.Key as CourseClass);
}
}
else
{
// insert class from second parent into new chromosome's calss table
if (n.classes.ContainsKey(it2.Key as CourseClass))
{
n.classes.Remove(it2.Key as CourseClass);
n.classes.Add(it2.Key as CourseClass, (int)it2.Value);
}
else
{
n.classes.Add(it2.Key as CourseClass, (int)it2.Value);
}
// all time-space slots of class are copied
for (int j = (it2.Key as CourseClass).GetDuration() - 1; j >= 0; j--)
{
n.slots[(int)it2.Value + j].Add(it2.Key as CourseClass);
}
}
}
// 计算交叉后染色体的适应值
n.CalculateFitness();
return n;
}
/// <summary>
/// 排出课表
/// </summary>
/// <param name="courseClasses">实际上课节数</param>
/// <param name="days">一周上课的天数</param>
/// <param name="nbr">一天上几节课</param>
/// <returns></returns>
public static Hashtable MakeSchedule(Hashtable courseClasses, int days, int nbr)
{
DataTable schedule = new DataTable();
List<Schedule> schedules = new List<Schedule>();
Hashtable courseSchedules = new Hashtable();
foreach (DictionaryEntry id in courseClasses)
{
if (((List<CourseClass>)id.Value).Count > days * nbr)
{
Exception ex = new Exception(((StudentsGroup)id.Key).GetName() + "要排的课太多了，排不下这么多课，请适量调整，谢谢Q！");
throw ex;
}
}
IDictionaryEnumerator it = courseClasses.GetEnumerator();
for (int j = 0; j < courseClasses.Count; j++)
{
it.MoveNext();
// 开始排某一班的课
Algorithm instance = Algorithm.GetInstance();
Schedule.DAYS_NUM = days; // 一周上几天课
Schedule.DAY_HOURS = nbr; // 一天上几节课
Schedule.COURSE_CLASS_NUM = ((List<CourseClass>)it.Value).Count; // 实际上课节数
Schedule best = new Schedule();
instance.GetSchedule(ref ScheduleService.professors, out best, (List<CourseClass>)it.Value);
courseSchedules.Add((StudentsGroup)it.Key, best);
instance.Destory();
}
return courseSchedules;
}
public static Algorithm GetInstance()
{
if (instance == null)
{
Schedule prototype = new Schedule(2, 2, 80, 3);
instance = new Algorithm(100, 8, 30, prototype);
}
return instance;
}
public void GetSchedule(ref List<Professor> professors, out Schedule best, List<CourseClass> courseList)
{
//if (prototype == null)
// return null;
//if (state == AlgorithmState.AS_RUNNING)
// return;
state = AlgorithmState.AS_RUNNING;
int i = 0;
for (; i < chromosomes.Capacity; i++)
{
chromosomes[i] = prototype.MakeNewFromPrototype(courseList);
//chromosomes.Add(prototype.MakeNewFromPrototype());
AddToBest(i);
}
// 设置当前代为0生代,属于原始人类
currentGeneration = 0;
while (true)
{
//if (state != AlgorithmState.AS_RUNNING)
//{
// break;
//}
// 获取最好的染色体，即获取最好的课表方案
best = GetBestChromosome();
// 当当前最好的染色体的fitness值大于等于1时，课表满足所有约束条件，停止排课
// 利用break跳出while(1)
if (best.GetFitness() >= 1)
{
//state = AlgorithmState.AS_CRITERIA_STOPPED;
for (int b = 0; b < best.GetSlots().Count; b++)
{
List <List< CourseClass>> slot = best.GetSlots();
if (slot[b].Count >= 1)
{
for (int p = 0; p < professors.Count; p++)
{
if (professors[p].GetId() == slot[b].First().GetProfessor().GetId())
{
professors[p].AddOccupiedTime(b);
}
}
}
}
return;
}
// 繁殖后代染色体
List<Schedule> offspring = new List<Schedule>(); // 定义后代染色体
offspring.Capacity = this.replaceByGeneration; // 设定后代染色体数量
for (int o = 0; o < offspring.Capacity; o++) // 初始化后代染色体
{
offspring.Add(new Schedule());
}
for (int j1 = 0; j1 < this.replaceByGeneration; j1++)
{
Schedule p2 = chromosomes[RandomNbr.GetRandomNbr() % chromosomes.Count];
Schedule p1 = chromosomes[RandomNbr.GetRandomNbr() % chromosomes.Count];
offspring[j1] = p1.Crossover(p2);
offspring[j1].Mutation();
}
// 用新繁殖后代染色体取代老一辈染色体,取代的个数为_replaceByGeneration
for (int j = 0; j < replaceByGeneration; j++)
{
int ci;
do
{
// 随机选择一个将被取代的染色体
ci = RandomNbr.GetRandomNbr() % chromosomes.Count;
// 如果该染色体属于最好的染色之一，则重新选一个;否则进行下面的操作，用新代取代老染色体
} while (IsInBest(ci));
// 删掉老一代的染色体，换成新的染色体
chromosomes[ci] = offspring[j];
// 看这个新生代染色体是否有足够条件成为最好的染色体之一
AddToBest(ci);
}// 到此，_replaceByGeneration个新生代染色体取代了_replaceByGeneration个老的染色体
currentGeneration++;
//Console.WriteLine("currentGeneration is " + currentGeneration);
//if (currentGeneration % 1000 == 0)
//{
// //Console.WriteLine("fitness is " + best.GetFitness());
// //System.Threading.Thread.Sleep(2000);
//}
}
}
