diff --git a/example/ex9.cpp b/example/ex9.cpp
index f177430..0bdeb5e 100644
--- a/example/ex9.cpp
+++ b/example/ex9.cpp
@@ -46,10 +46,11 @@ int main(int argc, char const *argv[])
     
     lgd_para p = e.default_lgd_para();
     p.beta = 1.2;
+    p.seed = 125;
     e.set_lgd_para(p);
 
     array<double> dist;
-    e.get_levy_distribution(dist, 125);
+    e.get_levy_distribution(dist);
 
     double m = dist.mean();
     double s = dist.std();
diff --git a/lib/optimization/lgd.cpp b/lib/optimization/lgd.cpp
index 6edfd93..ee46e48 100644
--- a/lib/optimization/lgd.cpp
+++ b/lib/optimization/lgd.cpp
@@ -30,7 +30,7 @@
 /**
  * Default parameter for the Lévy-Gradient Descent (L-GD) method.
  */
-static const gctl::lgd_para lgd_defparam = {1000, 0, 1e-5, 1.0, 1.5, 0.01, 1e-8, -1.0};
+static const gctl::lgd_para lgd_defparam = {1000, 0, 0, 1e-5, 1.0, 1.5, 0.01, 1e-8, -1.0};
 
 gctl::lgd_solver::lgd_solver()
 {
@@ -154,7 +154,7 @@ void gctl::lgd_solver::save_lgd_trace(std::string trace_file)
     return;
 }
 
-gctl::lgd_return_code gctl::lgd_solver::get_levy_distribution(array<double> &dist, unsigned int seed)
+gctl::lgd_return_code gctl::lgd_solver::get_levy_distribution(array<double> &dist)
 {
     if (lgd_param_.flight_times <= 0) return LGD_INVALID_MAX_ITERATIONS;
     if (lgd_param_.beta <= 1.0 || lgd_param_.beta >= 2.0) return LGD_INVALID_BETA;
@@ -164,8 +164,11 @@ gctl::lgd_return_code gctl::lgd_solver::get_levy_distribution(array<double> &dis
     double stddev_u = pow((gamma1*sin(0.5*GCTL_Pi*lgd_param_.beta))/
         (gamma2*lgd_param_.beta*pow(2, 0.5*(lgd_param_.beta-1.0))), 1.0/lgd_param_.beta);
 
-    if (seed == 0) seed = std::chrono::system_clock::now().time_since_epoch().count();
-    std::default_random_engine generator(seed);
+    unsigned int sd;
+    if (lgd_param_.seed == 0) sd = std::chrono::system_clock::now().time_since_epoch().count();
+    else sd = lgd_param_.seed;
+
+    std::default_random_engine generator(sd);
     std::normal_distribution<double> dist_u(0, stddev_u);
     std::normal_distribution<double> dist_v(0, lgd_param_.stddev_v);
     std::uniform_real_distribution<double> dist_s(1.0, 2.0);
@@ -386,8 +389,11 @@ gctl::lgd_return_code gctl::lgd_solver::lgd(array<double> &best_m, array<double>
     double gamma2 = tgamma(0.5*(lgd_param_.beta + 1.0));
     double stddev_u = pow((gamma1*sin(0.5*GCTL_Pi*lgd_param_.beta)) / (gamma2*lgd_param_.beta*pow(2, 0.5*(lgd_param_.beta-1.0))), 1.0/lgd_param_.beta);
 
-    unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();
-    std::default_random_engine generator(seed);
+    unsigned int sd;
+    if (lgd_param_.seed == 0) sd = std::chrono::system_clock::now().time_since_epoch().count();
+    else sd = lgd_param_.seed;
+
+    std::default_random_engine generator(sd);
     std::normal_distribution<double> dist_u(0, stddev_u);
     std::normal_distribution<double> dist_v(0, lgd_param_.stddev_v);
     std::uniform_real_distribution<double> dist_s(1.0, 2.0);
diff --git a/lib/optimization/lgd.h b/lib/optimization/lgd.h
index c6c7d2f..24fbfe6 100644
--- a/lib/optimization/lgd.h
+++ b/lib/optimization/lgd.h
@@ -88,6 +88,12 @@ namespace gctl
          */
         int batch;
 
+        /**
+         * Random seed for generating the Lévy distribution. Input zero for setting the seed according
+         * to the current time value. The default is 0.
+         */
+        int seed;
+
         /**
          * Epsilon for the mean convergence test. This parameter determines the accuracy 
          * with which the mean solution is to be found. The default is 1e-5.
@@ -176,11 +182,10 @@ namespace gctl
          * @brief 按照levy分布采样一组数据，参数可通过set_lgd_para函数设置
          * 
          * @param dist 返回的levy分布
-         * @param seed 随机种子
          * 
          * @return 状态代码
          */
-        lgd_return_code get_levy_distribution(array<double> &dist, unsigned int seed = 0);
+        lgd_return_code get_levy_distribution(array<double> &dist);
         
         lgd_para default_lgd_para();
         void set_lgd_para(const lgd_para &param);