/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double Fun, out double Delta)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.MaX_Iteration = int.Parse(Config[0][1]); // 最大迭代次数
this.Min_TolFun = double.Parse(Config[1][1]); // 结束条件
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
this.X_Dim = X_Init.Count();
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
this.X_Opt = new double[X_Dim];
for (int i = 0; i < X_Dim; i++)
{
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
}
Opt(); // 优化
Fun = Best_Fun;
Delta = Best_Delta;
return(X_Opt);
}
/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double Fun, out double Delta)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.Max_FEs = int.Parse(Config[0][1]); // 最大评价次数
this.Min_TolFun = double.Parse(Config[1][1]); // 结束条件
this.N_Pop = int.Parse(Config[2][1]); // 种群规模
this.Wmax = double.Parse(Config[3][1]); // 惯性权重Max
this.Wmin = double.Parse(Config[4][1]); // 惯性权重Min
this.C1 = double.Parse(Config[5][1]); // 加速因子1
this.C2 = double.Parse(Config[6][1]); // 加速因子2
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
this.X_Dim = X_Init.Length;
this.X = new double[N_Pop, X_Dim];
this.V = new double[N_Pop, X_Dim];
this.X_Init = new double[X_Dim];
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
this.Vmax = new double[X_Dim];
this.Vmin = new double[X_Dim];
this.Fun = new double[N_Pop]; // 各个个体的适应度
this.Delta = new double[N_Pop]; // 各个个体的约束违反度
this.gBest = new double[X_Dim];
this.pBest = new double[N_Pop, X_Dim];
this.pBest_Fun = new double[N_Pop];
this.pBest_Delta = new double[N_Pop];
this.X_Opt = new double[X_Dim];
this.res = new double[X_Dim + 1];
this.rd = new Random();
for (int i = 0; i < X_Dim; i++)
{
this.X_Init[i] = X_Init[i];
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
this.Vmax[i] = (this.X_Ub[i] - this.X_Lb[i]) * 0.5;
this.Vmin[i] = this.Vmax[i] * -1.0;
for (int k = 0; k < N_Pop; k++)
{
this.V[k, i] = this.Vmin[i] + G.rnd_uni(rd) * (this.Vmax[i] - this.Vmin[i]);
}
}
Opt(); // 优化
Fun = gBest_Fun;
Delta = gBest_Delta;
return(X_Opt);
}
/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double Fun, out double Delta)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.Max_FEs = int.Parse(Config[0][1]); // 最大评价次数
this.Min_TolFun = double.Parse(Config[1][1]); // 结束条件
this.N_Pop = int.Parse(Config[2][1]); // 种群规模
this.c = double.Parse(Config[3][1]);
this.p = double.Parse(Config[4][1]);
this.mu_F = double.Parse(Config[5][1]); // 缩放比例因子
this.mu_CR = double.Parse(Config[6][1]); // 交叉概率因子
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
this.Archive = new List <double[]>(); // 变量存档
this.S_F = new List <double>(); // F存档
this.S_CR = new List <double>(); // CR存档
this.X_Dim = X_Init.Length;
this.P_Pop = (int)Math.Ceiling(this.p * this.N_Pop);
this.X = new double[N_Pop, X_Dim];
this.X_P = new double[P_Pop, X_Dim];
this.X_Init = new double[X_Dim];
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
this.Fun = new double[N_Pop]; // 各个个体的适应度
this.Delta = new double[N_Pop]; // 各个个体的约束违反度
this.XBest = new double[X_Dim];
this.v = new double[X_Dim];
this.t = new double[X_Dim];
this.X_Opt = new double[X_Dim];
for (int i = 0; i < X_Dim; i++)
{
this.X_Init[i] = X_Init[i];
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
}
this.rd = new Random();
Opt(); // 优化
Fun = Best_Fun;
Delta = Best_Delta;
return(X_Opt);
}
/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double Fun, out double Delta)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.Max_FEs = int.Parse(Config[0][1]); // 最大评价次数
this.Min_TolFun = double.Parse(Config[1][1]); // 结束条件
this.N_Pop = int.Parse(Config[2][1]); // 种群规模
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
this.X_Dim = X_Init.Length;
this.X = new double[N_Pop, X_Dim];
this.X_Init = new double[X_Dim];
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
this.Fun = new double[N_Pop]; // 各个个体的适应度
this.Delta = new double[N_Pop]; // 各个个体的约束违反度
this.v = new double[X_Dim];
this.v1 = new double[X_Dim];
this.t1 = new double[X_Dim];
this.v2 = new double[X_Dim];
this.t2 = new double[X_Dim];
this.v3 = new double[X_Dim];
this.t3 = new double[X_Dim];
this.X_Opt = new double[X_Dim];
for (int i = 0; i < X_Dim; i++)
{
this.X_Init[i] = X_Init[i];
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
}
this.rd = new Random();
Opt(); // 优化
Fun = Best_Fun;
Delta = Best_Delta;
return(X_Opt);
}
/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double Fun, out double Delta)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.Max_FEs = int.Parse(Config[0][1]); // 最大评价次数
this.Min_TolFun = double.Parse(Config[1][1]); // 结束条件
this.Init_Step_Ratio = double.Parse(Config[2][1]); // 初始步长比
this.Min_Step_Ratio = double.Parse(Config[3][1]); // 最小步长比
this.Alpha = double.Parse(Config[4][1]); // 加速因子
this.Beta = double.Parse(Config[5][1]); // 收缩因子
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
this.X_Dim = X_Init.Length;
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
this.X = new double[X_Dim]; // 变量
this.Y = new double[X_Dim]; // 探测变量
this.Y_tmp = new double[X_Dim]; // 探测变量
this.Last_X = new double[X_Dim]; // 模式变量
this.X_Opt = new double[X_Dim];
this.Step = new double[X_Dim]; // 步长
this.Min_Step = new double[X_Dim]; // 最小步长
for (int i = 0; i < X_Dim; i++)
{
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
this.X[i] = X_Init[i];
this.Step[i] = (this.X_Ub[i] - this.X_Lb[i]) / this.Init_Step_Ratio;
this.Min_Step[i] = (this.X_Ub[i] - this.X_Lb[i]) / this.Min_Step_Ratio;
}
Opt(); // 优化
Fun = Best_Fun;
Delta = Best_Delta;
return(X_Opt);
}
/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double Fun, out double Delta)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.Max_FEs = int.Parse(Config[0][1]); // 最大评价次数
this.Min_TolFun = double.Parse(Config[1][1]); // 结束条件
this.Step_Ratio = double.Parse(Config[2][1]); // 初始步长比
this.Epsilon = double.Parse(Config[3][1]); // 结束条件
this.Alpha = double.Parse(Config[4][1]); // 反射因子
this.Beta = double.Parse(Config[5][1]); // 收缩因子
this.Gamma = double.Parse(Config[6][1]); // 扩大因子
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
this.X_Dim = X_Init.Length;
this.X = new double[X_Dim + 1, X_Dim]; // 变量
this.Delta = new double[X_Dim + 1]; // 约束违反度
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
this.X_Center = new double[X_Dim]; // 重心点
this.X_R = new double[X_Dim]; // 反射点
this.X_E = new double[X_Dim]; // 扩大点
this.X_C = new double[X_Dim]; // 收缩点
this.X_Opt = new double[X_Dim]; // 最优解
this.Step = new double[X_Dim]; // 步长
this.Fun = new double[X_Dim + 1]; // 目标值
for (int i = 0; i < X_Dim; i++)
{
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
this.Step[i] = (this.X_Ub[i] - this.X_Lb[i]) / this.Step_Ratio;
this.X[0, i] = X_Init[i];
for (int j = 0; j < X_Dim; j++)
{
if (i == j)
{
this.X[j + 1, i] = X_Init[i] + Step[i];
this.X[j + 1, i] = G.BoundConstraint(this.X[j + 1, i], this.X_Lb[i], this.X_Ub[i]);
}
else
{
this.X[j + 1, i] = X_Init[i];
}
}
}
Opt(); // 优化
Fun = Best_Fun;
Delta = Best_Delta;
return(X_Opt);
}
/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double Fun, out double Delta)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.Max_FEs = int.Parse(Config[0][1]); // 最大评价次数
this.Min_TolFun = double.Parse(Config[1][1]); // 结束条件
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
var M = Matrix <double> .Build; // 生成矩阵
//var V = Vector<double>.Build; // 生成向量
this.X_Dim = X_Init.Length;
this.X = new double[X_Dim];
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
double[] X_Diff = new double[X_Dim];
for (int i = 0; i < X_Dim; i++)
{
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
X_Diff[i] = this.X_Ub[i] - this.X_Lb[i];
}
this.X_Opt = new double[X_Dim];
this.xmeanw = M.Dense(X_Dim, 1);
this.xold = M.Dense(X_Dim, 1);
this.zmeanw = M.Dense(X_Dim, 1);
this.flginiphase = true;
this.sigma = 0.25; // Initial step size
this.Min_sigma = 1.0e-15; // Minimal step size
this.Max_sigma = X_Diff.Max() / Math.Sqrt((double)X_Dim);
for (int i = 0; i < X_Dim; i++)
{
this.xmeanw[i, 0] = X_Init[i];
}
this.lambda = 4 + (int)Math.Floor(3.0 * Math.Log((double)X_Dim));
this.mu = (int)Math.Floor((double)lambda / 2.0);
this.arweights = M.Dense(mu, 1);
for (int i = 0; i < mu; i++)
{
this.arweights[i, 0] = Math.Log((lambda + 1.0) / 2.0) - Math.Log(i + 1.0);
}
this.cc = 4.0 / (X_Dim + 4.0);
this.ccov = 2.0 / Math.Pow((X_Dim + Math.Pow(2.0, 0.5)), 2.0);
this.cs = 4.0 / (X_Dim + 4.0);
this.damp = (1.0 - Math.Min(0.7, X_Dim * lambda / Max_FEs)) / cs + 1.0;
this.B = M.DenseIdentity(X_Dim);
this.D = M.DenseIdentity(X_Dim);
this.BD = M.Dense(X_Dim, X_Dim);
this.C = M.Dense(X_Dim, X_Dim);
this.CC = M.Dense(X_Dim, X_Dim);
this.BD = B * D;
this.C = BD * BD.Transpose();
this.pc = M.Dense(X_Dim, 1);
this.ps = M.Dense(X_Dim, 1);
this.cw = arweights.RowSums().Sum() / arweights.L2Norm();
this.chiN = Math.Pow(X_Dim, 0.5) * (1.0 - 1.0 / (4.0 * X_Dim) + 1.0 / (21.0 * Math.Pow(X_Dim, 2.0)));
this.arz = M.Dense(X_Dim, lambda);
this.arx = M.Dense(X_Dim, lambda);
this.arz_mu = M.Dense(X_Dim, mu);
this.arx_mu = M.Dense(X_Dim, mu);
this.Fun = new double[lambda]; // 各个个体的适应度
this.Delta = new double[lambda]; // 各个个体的约束违反度
this.Index = new int[lambda]; // 各个个体的序号
this.rd = new Random();
Opt(); // 优化
Fun = Best_Fun;
Delta = Best_Delta;
return(X_Opt);
}
/// <summary>
/// 开始优化
/// </summary>
/// <param name="ConfigFile">算法参数配置文件路径</param>
/// <param name="X_Init">优化变量初值</param>
/// <param name="X_MaxStep">优化变量最大步长</param>
/// <param name="X_Lb">优化变量下限</param>
/// <param name="X_Ub">优化变量上限</param>
/// <returns>使适应度函数最小的变量值</returns>
/// <returns>最优目标函数</returns>
public double[,] StartOpt(string ConfigFile, double[] X_Init, double[] X_MaxStep, double[] X_Lb, double[] X_Ub, out double[,] Fun)
{
if (X_Init.Length != X_MaxStep.Length || X_Init.Length != X_Lb.Length || X_Init.Length != X_Ub.Length)
{
throw new Exception("Variable number are not set correctly!");
}
this.G = new General();
List <string[]> Config = new List <string[]>();
try
{
Config = G.ReadCSV(ConfigFile);
this.Max_FEs = int.Parse(Config[0][1]); // 最大评价次数
this.N_Pop = int.Parse(Config[1][1]); // 种群规模
}
catch (System.Exception ex)
{
throw new Exception("ConfigFile are not existed!");
}
this.Obj_Dim = fitness.GetObjectiveNumber();
//this.Obj_Dim = 3;
this.Z = ReferencePoint(N_Pop, Obj_Dim, out N_Pop); // 生成参考点
this.X_Dim = X_Init.Length;
this.ProM = 1.0 / X_Dim;
this.Zmin = new double[Obj_Dim];
this.Zmax = new double[Obj_Dim];
this.X = new double[N_Pop, X_Dim];
this.X_Offspring = new double[N_Pop, X_Dim];
this.X_R = new double[N_Pop * 2, X_Dim];
this.X_Init = new double[X_Dim];
this.X_Lb = new double[X_Dim];
this.X_Ub = new double[X_Dim];
this.Fun = new double[N_Pop, Obj_Dim]; // 各个个体的适应度
this.Fun_Offspring = new double[N_Pop, Obj_Dim];
this.Fun_R = new double[N_Pop * 2, Obj_Dim];
this.Delta = new double[N_Pop]; // 各个个体的约束违反度
this.Delta_Offspring = new double[N_Pop];
this.Delta_R = new double[N_Pop * 2];
this.FrontValue = new int[N_Pop * 2];
for (int i = 0; i < X_Dim; i++)
{
this.X_Init[i] = X_Init[i];
this.X_Lb[i] = Math.Max(X_Init[i] - X_MaxStep[i], X_Lb[i]);
this.X_Ub[i] = Math.Min(X_Init[i] + X_MaxStep[i], X_Ub[i]);
}
for (int i = 0; i < Obj_Dim; i++)
{
Zmin[i] = double.MaxValue;
}
this.rd = new Random();
//this.rd1 = new Random();
//this.rd2 = new Random();
//this.rd3 = new Random();
//this.rd4 = new Random();
//this.rd5 = new Random();
Opt(); // 优化
Fun = new double[N_Pop, Obj_Dim];
for (int i = 0; i < N_Pop; i++)
{
for (int j = 0; j < Obj_Dim; j++)
{
Fun[i, j] = this.Fun[i, j];
}
}
return(this.X);
}
