diff --git a/UQPyL/__init__.py b/UQPyL/__init__.py
index 649681fc..1c43282e 100644
--- a/UQPyL/__init__.py
+++ b/UQPyL/__init__.py
@@ -1,6 +1,6 @@
 from . import problems, surrogates, optimization, sensibility, DoE, utility
 
-__version__ = "2.0.1"
+__version__ = "2.0.4"
 __author__ = "wmtSky"
 
 __all__=[
diff --git a/UQPyL/optimization/asmo.py b/UQPyL/optimization/asmo.py
index 6720889e..78906d38 100644
--- a/UQPyL/optimization/asmo.py
+++ b/UQPyL/optimization/asmo.py
@@ -3,6 +3,7 @@
 from typing import Optional
 
 from .sce_ua import SCE_UA
+from .pso import PSO
 from ..DoE import LHS
 from ..problems import Problem
 from ..surrogates import Surrogate
@@ -46,11 +47,11 @@ def __init__(self, problem: Problem, surrogate: Surrogate,
         #construct optimization problem to combine surrogate and algorithm
         self.subProblem=Problem(self.surrogate.predict, self.n_input, 1, self.ub, self.lb)
         
-    def run(self,maxFE=1000, Tolerate=0.001, maxTolerateTime=50, oneStep=False):
+    def run(self, oneStep=False):
         '''
         main procedure
         ''' 
-        show_process=tqdm(total=maxFE)
+        show_process=tqdm(total=self.maxFE)
         FE=0
         TT=0
         n_input=self.n_input
@@ -65,8 +66,8 @@ def run(self,maxFE=1000, Tolerate=0.001, maxTolerateTime=50, oneStep=False):
         XPop=self.x_init
         YPop=self.y_init
         
-        fe=YPop.shape[0]
-        show_process.update(fe)
+        FE=YPop.shape[0]
+        show_process.update(FE)
         ###
         idx=np.argsort(YPop, axis=0)
         BestY=YPop[idx[0,0],0]
@@ -78,11 +79,11 @@ def run(self,maxFE=1000, Tolerate=0.001, maxTolerateTime=50, oneStep=False):
         # history_BestY.append(BestY)
         
         if (oneStep==False):
-            while fe<self.maxFE and TT<self.maxTolerateTime:
+            while FE<self.maxFE and TT<self.maxTolerateTime:
                 show_process.update(1)
                 # Build surrogate model
                 self.surrogate.fit(XPop, YPop)
-                res=SCE_UA(self.subProblem).run()
+                res=PSO(self.subProblem).run()
                 BestX_SM=res['best_dec']
                 
                 TempY=self.evaluate(BestX_SM)
@@ -91,8 +92,10 @@ def run(self,maxFE=1000, Tolerate=0.001, maxTolerateTime=50, oneStep=False):
                 YPop=np.vstack((YPop,TempY))
                 
                 if TempY[0,0]<BestY:
-                    BestY=np.copy(TempY)
+                    BestY=np.copy(TempY[0,0])
                     BestX=np.copy(BestX_SM)
+                    history_best_decs[FE]=BestX
+                    history_best_objs[FE]=BestY
         else:
             self.surrogate.fit(XPop, YPop)
             res=SCE_UA(self.subProblem).run()
@@ -100,17 +103,17 @@ def run(self,maxFE=1000, Tolerate=0.001, maxTolerateTime=50, oneStep=False):
             
             TempY=self.evaluate(BestX_SM)
             
-            fe+=1
+            FE+=1
             XPop=np.vstack((XPop,BestX_SM))
             YPop=np.vstack((YPop,TempY))
             
             if TempY[0,0]<BestY:
                 BestY=np.copy(TempY)
                 BestX=np.copy(BestX_SM)
-                history_best_decs[fe]=BestX
-                history_best_objs[fe]=BestY
+                history_best_decs[FE]=BestX
+                history_best_objs[FE]=BestY
         
-        Result={'best_dec':BestX, 'best_obj':BestY, 'history_best_decs': history_best_decs, 'history_best_objs':history_best_objs ,'FE':fe}
+        Result={'best_dec':BestX, 'best_obj':BestY, 'history_best_decs': history_best_decs, 'history_best_objs':history_best_objs ,'FE':FE}
         
         return Result
             
diff --git a/examples/example_optimization.py b/examples/example_optimization.py
index d20d4a12..eea6fc8e 100644
--- a/examples/example_optimization.py
+++ b/examples/example_optimization.py
@@ -19,74 +19,75 @@
 import numpy as np
 
 ################1. Genetic Algorithm (GA) - Single objective################
-print('################1. Genetic Algorithm (GA) - Single objective################')
-from UQPyL.optimization import GA
-from UQPyL.problems import Sphere
+# print('################1. Genetic Algorithm (GA) - Single objective################')
+# from UQPyL.optimization import GA
+# from UQPyL.problems import Sphere
 
-problem=Sphere(n_input=30, ub=100, lb=-100)
-ga=GA(problem, n_samples=50)
-res=ga.run()
-print('Best objective:', res['best_obj'])
-print('Best decisions:', res['best_dec'])
-print('FE:', res['FEs'])
+# problem=Sphere(n_input=30, ub=100, lb=-100)
+# ga=GA(problem, n_samples=50)
+# res=ga.run()
+# print('Best objective:', res['best_obj'])
+# print('Best decisions:', res['best_dec'])
+# print('FE:', res['FEs'])
  
-import matplotlib.pyplot as plt
-FEs_objs=res['history_best_objs']
-FEs=list(FEs_objs.keys())
-objs=list(FEs_objs.values())
-plt.plot(FEs, np.log10(objs))
+# import matplotlib.pyplot as plt
+# FEs_objs=res['history_best_objs']
+# FEs=list(FEs_objs.keys())
+# objs=list(FEs_objs.values())
+# plt.plot(FEs, np.log10(objs))
 
 
 ################2. SCE-UA - Single objective################
-print('################2. SCE-UA - Single objective################')
-from UQPyL.optimization import SCE_UA
-problem=Sphere(n_input=30, ub=100, lb=-100)
-sce=SCE_UA(problem)
-res=sce.run()
-print('Best objective:', res['best_obj'])
-print('Best decisions:', res['best_dec'])
-print('FE:', res['FEs'])
+# print('################2. SCE-UA - Single objective################')
+# from UQPyL.optimization import SCE_UA
+# problem=Sphere(n_input=30, ub=100, lb=-100)
+# sce=SCE_UA(problem)
+# res=sce.run()
+# print('Best objective:', res['best_obj'])
+# print('Best decisions:', res['best_dec'])
+# print('FE:', res['FEs'])
 
-objs=res['history_best_objs']
-FEs_objs=res['history_best_objs']
-FEs=list(FEs_objs.keys())
-objs=list(FEs_objs.values())
-plt.plot(FEs, np.log10(objs))
+# objs=res['history_best_objs']
+# FEs_objs=res['history_best_objs']
+# FEs=list(FEs_objs.keys())
+# objs=list(FEs_objs.values())
+# plt.plot(FEs, np.log10(objs))
 
 ###############3. PSO - Single objective################
-print('###############3. PSO - Single objective################')
-from UQPyL.optimization import PSO
+# print('###############3. PSO - Single objective################')
+# from UQPyL.optimization import PSO
+# from UQPyL.problems import Sphere
+# problem=Sphere(n_input=30, ub=100, lb=-100)
+# pso=PSO(problem, n_sample=50, w=0.5, c1=1.5, c2=1.5)
+# res=pso.run()
+# print('Best objective:', res['best_obj'])
+# print('Best decisions:', res['best_decs'])
+# print('FE:', res['FEs'])
+
+# objs=res['history_best_objs']
+# FEs_objs=res['history_best_objs']
+# FEs=list(FEs_objs.keys())
+# objs=list(FEs_objs.values())
+# plt.plot(FEs, np.log10(objs))
+
+# plt.title('Best Objective Over Iterations')
+# plt.xlabel('Function Evbaluations')
+# plt.ylabel('Best Objective (log10)')
+# plt.show()
+################3. ASMO - Single-objective - Surrogate-assisted################
+print('################3. ASMO - Single-objective################')
+from UQPyL.optimization import ASMO
+from UQPyL.surrogates import RBF
+from UQPyL.surrogates.rbf_kernels import Cubic
 from UQPyL.problems import Sphere
 problem=Sphere(n_input=30, ub=100, lb=-100)
-pso=PSO(problem, n_sample=50, w=0.5, c1=1.5, c2=1.5)
-res=pso.run()
+rbf=RBF(kernel=Cubic())
+asmo=ASMO(problem, rbf, n_init=50)
+res=asmo.run(maxFE=1000)
 print('Best objective:', res['best_obj'])
-print('Best decisions:', res['best_decs'])
+print('Best decisions:', res['best_dec'])
 print('FE:', res['FEs'])
 
-objs=res['history_best_objs']
-FEs_objs=res['history_best_objs']
-FEs=list(FEs_objs.keys())
-objs=list(FEs_objs.values())
-plt.plot(FEs, np.log10(objs))
-
-plt.title('Best Objective Over Iterations')
-plt.xlabel('Function Evbaluations')
-plt.ylabel('Best Objective (log10)')
-plt.show()
-################3. ASMO - Single-objective - Surrogate-assisted################
-# print('################3. ASMO - Single-objective################')
-# from UQPyL.optimization import ASMO
-# from UQPyL.surrogates import RBF
-# from UQPyL.surrogates.rbf_kernels import Cubic
-# problem=Sphere(n_input=30, ub=100, lb=-100)
-# rbf=RBF(kernel=Cubic())
-# asmo=ASMO(problem, rbf, n_init=50)
-# res=asmo.run(maxFE=1000)
-# print('Best objective:', res['best_obj'])
-# print('Best decisions:', res['best_dec'])
-# print('FE:', res['FEs'])
-
 #############4. NSGA-II - Multi-objective#################
 # print('#############4. NSGA-II - Multi-objective#################')
 # from UQPyL.optimization import NSGAII
@@ -101,48 +102,48 @@
 # plt.show()
 
 #############5. MOEAD - Multi-objective#################
-print('#############5. MOEAD - Multi-objective#################')
-from UQPyL.optimization import MOEA_D
-from UQPyL.problems import ZDT6
-problem=ZDT6(n_input=30)
-optimum=problem.get_PF()
-moead=MOEA_D(problem, aggregation_type='TCH_M',maxFEs=50000, maxIters=1000)
-_, res=moead.run()
-import matplotlib.pyplot as plt
-plt.scatter(res[:,0], res[:,1])
-plt.plot(optimum[:,0], optimum[:,1], 'r')
-plt.show()
+# print('#############5. MOEAD - Multi-objective#################')
+# from UQPyL.optimization import MOEA_D
+# from UQPyL.problems import ZDT6
+# problem=ZDT6(n_input=30)
+# optimum=problem.get_PF()
+# moead=MOEA_D(problem, aggregation_type='TCH_M',maxFEs=50000, maxIters=1000)
+# _, res=moead.run()
+# import matplotlib.pyplot as plt
+# plt.scatter(res[:,0], res[:,1])
+# plt.plot(optimum[:,0], optimum[:,1], 'r')
+# plt.show()
 
 #############5. MO_ASMO - Multi-objective - Surrogate-assisted#################
-from UQPyL.optimization import MOASMO
-from UQPyL.problems import ZDT1
-from UQPyL.surrogates import RBF, KRG
-from UQPyL.surrogates import Mo_Surrogates
-from UQPyL.surrogates.rbf_kernels import Cubic
-from UQPyL.surrogates.krg_kernels import Guass_Kernel
-import matplotlib.pyplot as plt
+# from UQPyL.optimization import MOASMO
+# from UQPyL.problems import ZDT1
+# from UQPyL.surrogates import RBF, KRG
+# from UQPyL.surrogates import Mo_Surrogates
+# from UQPyL.surrogates.rbf_kernels import Cubic
+# from UQPyL.surrogates.krg_kernels import Guass_Kernel
+# import matplotlib.pyplot as plt
 
-rbf1=RBF(kernel=Cubic())
-rbf2=RBF(kernel=Cubic())
-guass1=Guass_Kernel(theta=1e-3, theta_lb=1e-5, theta_ub=1, heterogeneous=True)
-guass2=Guass_Kernel(theta=1e-3, theta_lb=1e-5, theta_ub=1, heterogeneous=True)
-krg1=KRG(kernel=guass1)
-krg2=KRG(kernel=guass2)
-mo_surr=Mo_Surrogates(2,[krg1, krg2])
-problem=ZDT1(n_input=30)
-mo_amso=MOASMO(problem=problem, surrogates=mo_surr, maxFEs=300, advance_infilling=False)
-res=mo_amso.run()
-y=res[1]
-fig, axs = plt.subplots(1, 2) 
-axs[0].scatter(y[:,0], y[:,1], color='b')
-axs[0].set_title('Advanced')
-rbf1=RBF(kernel=Cubic())
-rbf2=RBF(kernel=Cubic())
-mo_surr=Mo_Surrogates(2,[rbf1, rbf2])
-problem=ZDT1(n_input=30)
-mo_amso=MOASMO(problem=problem, surrogates=mo_surr, maxFEs=300, advance_infilling=False)
-res=mo_amso.run()
-yy=res[1]
-axs[1].scatter(yy[:,0], yy[:,1], color='r')
-axs[1].set_title('Origin')
-plt.show()
\ No newline at end of file
+# rbf1=RBF(kernel=Cubic())
+# rbf2=RBF(kernel=Cubic())
+# guass1=Guass_Kernel(theta=1e-3, theta_lb=1e-5, theta_ub=1, heterogeneous=True)
+# guass2=Guass_Kernel(theta=1e-3, theta_lb=1e-5, theta_ub=1, heterogeneous=True)
+# krg1=KRG(kernel=guass1)
+# krg2=KRG(kernel=guass2)
+# mo_surr=Mo_Surrogates(2,[krg1, krg2])
+# problem=ZDT1(n_input=30)
+# mo_amso=MOASMO(problem=problem, surrogates=mo_surr, maxFEs=300, advance_infilling=False)
+# res=mo_amso.run()
+# y=res[1]
+# fig, axs = plt.subplots(1, 2) 
+# axs[0].scatter(y[:,0], y[:,1], color='b')
+# axs[0].set_title('Advanced')
+# rbf1=RBF(kernel=Cubic())
+# rbf2=RBF(kernel=Cubic())
+# mo_surr=Mo_Surrogates(2,[rbf1, rbf2])
+# problem=ZDT1(n_input=30)
+# mo_amso=MOASMO(problem=problem, surrogates=mo_surr, maxFEs=300, advance_infilling=False)
+# res=mo_amso.run()
+# yy=res[1]
+# axs[1].scatter(yy[:,0], yy[:,1], color='r')
+# axs[1].set_title('Origin')
+# plt.show()
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index b1cb4c7e..d0eec1b4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "UQPyL"
-version = "2.0.3"
+version = "2.0.4"
 authors = [
     {name = "wmtSky", email = "wmtsky@hhu.edu.cn"}
 ]