Пример #1
0
        /// <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);
        }