布谷鸟优化算法C++
#include <iostream>
#include <vector>
#include <cmath>
#include <random>
#include <time.h>
#include <fstream>
#define pi acos(-1)
//5只布谷鸟
constexpr int NestNum = 40;
//pi值
//规定X,Y 的取值范围
constexpr double X_max = 5;
constexpr double X_min = 0;
constexpr double Y_max = 5;
constexpr double Y_min = 0;
//最大迭代次数
constexpr int MaxIterationTimes = 300;
//被宿主发现的概率
constexpr double Pa = 0.25;
//自变量结构体
struct Nest {
double x;
double y;
double fitness;
};
void fitFunc(Nest& nest);
int findBetterNest(std::vector<Nest>&);
std::vector<Nest> levy(std::vector<Nest> OldNestPop, std::size_t bestNum);
std::vector<Nest> RandomAbandonPaNestPop(std::vector<Nest> OldNestPop);
//随机数引擎
static std::default_random_engine e(time(0));
static std::uniform_real_distribution<double> u(0, 1);
int main(void)
{
bool flag_output = false;
double Xold;
double Xnew;
double Yold;
double Ynew;
std::ofstream outfileX("D:\\cuckoo\\cuckooX.txt");
std::ofstream outfileY("D:\\cuckoo\\cuckooY.txt");
std::ofstream outfileZ("D:\\cuckoo\\cuckooZ.txt");
//现在的鸟巢群
std::vector<Nest> current_nestPop;
//迭代次数
int num = 0;
//最优鸟巢
int BestNestCurrent;
//初始化
for (int i = 0; i < NestNum; ++i)
{
Nest nestinitial;
nestinitial.x = (X_max - X_min) * u(e) + X_min;
nestinitial.y = (Y_max - Y_min) * u(e) + Y_min;
fitFunc(nestinitial);
current_nestPop.push_back(nestinitial);
}
//for (auto i : nestPop)
//{
// std::cout << i.fitness << std::endl;
//}
//寻找最优个体
BestNestCurrent = findBetterNest(current_nestPop);
outfileX << current_nestPop[BestNestCurrent].x << std::endl;
outfileY << current_nestPop[BestNestCurrent].y << std::endl;
outfileZ << current_nestPop[BestNestCurrent].fitness << std::endl;
while (num < MaxIterationTimes)
{
//储存上次的最优解的X,Y
Xold = current_nestPop[BestNestCurrent].x;
Yold = current_nestPop[BestNestCurrent].y;
//levy飞行--位置更新
std::vector<Nest> NewNestPop = levy(current_nestPop, BestNestCurrent);
//用适应值较好的鸟窝位置替换适应值较差的鸟窝位置
for (decltype(NewNestPop.size()) i = 0; i < NewNestPop.size(); ++i)
{
if (i != BestNestCurrent && NewNestPop[i].fitness < current_nestPop[i].fitness)
{
current_nestPop[i] = NewNestPop[i];
}
}//此时得到更优的鸟窝位置
//存安去险 保留鸟窝中被发现概率较小的鸟窝位置，并随机改变发现概率较大的鸟窝位置
NewNestPop = RandomAbandonPaNestPop(current_nestPop);
for (decltype(NewNestPop.size()) i = 0; i < NewNestPop.size(); ++i)
{
if (i != BestNestCurrent && NewNestPop[i].fitness < current_nestPop[i].fitness)
{
current_nestPop[i] = NewNestPop[i];
}
}//此时得到更优的鸟窝位置
BestNestCurrent = findBetterNest(current_nestPop);//现在的最优鸟巢位置
Xnew = current_nestPop[BestNestCurrent].x;
Ynew = current_nestPop[BestNestCurrent].y;
if (Xnew != Xold || Ynew != Yold)
{
outfileX << current_nestPop[BestNestCurrent].x << std::endl;
outfileY << current_nestPop[BestNestCurrent].y << std::endl;
}
outfileZ << current_nestPop[BestNestCurrent].fitness << std::endl;
/*std::cout << current_nestPop[BestNestCurrent].fitness << std::endl;
std::cout << "(x,y)" << '(' << current_nestPop[BestNestCurrent].x << ',' << current_nestPop[BestNestCurrent].y << ')' << std::endl;*/
//outfileX << current_nestPop[BestNestCurrent].x << std::endl;
//outfileY << current_nestPop[BestNestCurrent].y << std::endl;
//outfileZ << current_nestPop[BestNestCurrent].fitness << std::endl;
num++;
}
std::cout << current_nestPop[BestNestCurrent].fitness << std::endl;
return 0;
}
void fitFunc(Nest& nest)
{
nest.fitness = -sin(nest.x) * pow(sin(nest.x * nest.x / pi), 20) - sin(nest.y) * pow(sin(2 * nest.y * nest.y / pi), 20);
//nest.fitness = -(nest.x - 1) * (nest.x - 1) + 1;
}
int findBetterNest(std::vector<Nest>& nestPop)
{
int BestNum = 0;
for (decltype(nestPop.size()) i = 0; i < nestPop.size(); ++i)
{
if (nestPop[i].fitness < nestPop[BestNum].fitness)
{
BestNum = i;
}
}
return BestNum;
}
std::vector<Nest> levy(std::vector<Nest> OldNestPop,std::size_t bestNum)
{
double beta = 1.5;
// double alpha = 0.01 * R(e);//有的论文写
double alpha = 0.4;
double sigma_u = pow((tgamma(1 + beta) * sin(pi * beta / 2)) / (beta * tgamma((1 + beta) / 2) * pow(2, (beta - 1) / 2)), 1 / beta);
double sigma_v = 1;
static std::normal_distribution<double> R(0, sigma_u);
static std::normal_distribution<double> R1(0, sigma_v);
for (auto& i : OldNestPop)
{
//前面的系数是保证最优鸟巢不会进行levy飞行
double stepX = (i.x - OldNestPop[bestNum].x) * R(e) / (pow(abs(R1(e)), 1 / beta));
double stepY = (i.x - OldNestPop[bestNum].x) * R(e) / (pow(abs(R1(e)), 1 / beta));
//按范围更新X
if (i.x + alpha * stepX > X_max)
{
i.x = X_max;
}
else if(i.x + alpha * stepX < X_min)
{
i.x = X_min;
}
else
{
i.x = i.x + alpha * stepX;
}
//按范围更新Y
if (i.y + alpha * stepY > Y_max)
{
i.y = Y_max;
}
else if (i.y + alpha * stepY < Y_min)
{
i.y = Y_min;
}
else
{
i.y = i.y + alpha * stepY;
}
fitFunc(i);
}
return OldNestPop;
}
std::vector<Nest> RandomAbandonPaNestPop(std::vector<Nest> OldNestPop)
{
double step_sizeX = 0;
double step_sizeY = 0;
static std::uniform_int_distribution<int> randomInt(0, OldNestPop.size() - 1);
for(decltype(OldNestPop.size()) i = 0;i < OldNestPop.size();++i)
{
if (u(e) < Pa)//被宿主发现了，要重新寻找新巢
{
step_sizeX = u(e) * (OldNestPop[randomInt(e)].x - OldNestPop[randomInt(e)].x);
step_sizeY = u(e) * (OldNestPop[randomInt(e)].y - OldNestPop[randomInt(e)].y);
if (OldNestPop[i].x + step_sizeX > X_max)
{
OldNestPop[i].x = X_max;
}
else if(OldNestPop[i].x + step_sizeX < X_min)
{
OldNestPop[i].x = X_min;
}
else
{
OldNestPop[i].x += step_sizeX;
}
if (OldNestPop[i].y + step_sizeY > Y_max)
{
OldNestPop[i].y = Y_max;
}
else if (OldNestPop[i].y + step_sizeY < Y_min)
{
OldNestPop[i].y = Y_min;
}
else
{
OldNestPop[i].y += step_sizeY;
}
fitFunc(OldNestPop[i]);
}
}
return OldNestPop;
}