diff --git a/.travis.yml b/.travis.yml
index 0d83694..dd1cf80 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -22,7 +22,7 @@ script:
   - coverage run -p examples/demo_sa_tsp.py examples/data/nctu.csv
   - coverage run -p examples/demo_aca_tsp.py
   - coverage run -p examples/demo_ia.py
-  - coverage run -p examples/demo_asfs.py
+  - coverage run -p examples/demo_afsa.py
   - coverage run -p examples/demo_pso_ani.py
   - coverage run -p examples/demo_de.py
 
diff --git a/README.md b/README.md
index 5d7185b..81b30af 100644
--- a/README.md
+++ b/README.md
@@ -451,19 +451,19 @@ print('best routine:', best_points, 'best_distance:', best_distance)
 
 ![IA](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ia2.png?raw=true)
 
-## 7. artificial fish swarm algorithm (AFSA)
--> Demo code: [examples/demo_asfs.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_asfs.py#L1)
+## 7. Artificial Fish Swarm Algorithm (AFSA)
+-> Demo code: [examples/demo_afsa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_afsa.py#L1)
 ```python
 def func(x):
     x1, x2 = x
     return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2
 
 
-from sko.ASFA import ASFA
+from sko.AFSA import AFSA
 
-asfa = ASFA(func, n_dim=2, size_pop=50, max_iter=300,
+afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300,
             max_try_num=100, step=0.5, visual=0.3,
             q=0.98, delta=0.5)
-best_x, best_y = asfa.run()
+best_x, best_y = afsa.run()
 print(best_x, best_y)
 ```
diff --git a/docs/en/README.md b/docs/en/README.md
index 67984f1..7131f2e 100644
--- a/docs/en/README.md
+++ b/docs/en/README.md
@@ -32,10 +32,6 @@ cd scikit-opt
 pip install .
 ```
 
-## News:
-All algorithms will be available on ~~/Spark/Pytorch~~ **TensorFlow** on version ~~0.4~~ **1.x**, getting parallel performance.  
-Have fun!
-
 
 ### Feature1: UDF
 
@@ -92,10 +88,9 @@ print('best_x:', best_x, '\n', 'best_y:', best_y)
 ```
 
 > Until Now, the **udf** surport `crossover`, `mutation`, `selection`, `ranking` of GA
-
 > scikit-opt provide a dozen of operators, see [here](https://github.com/guofei9987/scikit-opt/tree/master/sko/operators)
 
-> For advanced users, there is another OOP style:
+For advanced users:
 
 -> Demo code: [examples/demo_ga_udf.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L31)
 ```python
@@ -118,8 +113,11 @@ my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -
 best_x, best_y = my_ga.run()
 print('best_x:', best_x, '\n', 'best_y:', best_y)
 ```
+### feature2: GPU computation
+ We are developing GPU computation, which will be stable on version 1.0.0  
+An example is already available: [https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py)
 
-###  feature2: continue to run
+###  feature3: continue to run
 (New in version 0.3.6)  
 Run an algorithm for 10 iterations, and then run another 20 iterations base on the 10 iterations before:
 ```python
@@ -433,19 +431,19 @@ print('best routine:', best_points, 'best_distance:', best_distance)
 
 ![IA](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ia2.png?raw=true)
 
-## 7. artificial fish swarm algorithm (AFSA)
--> Demo code: [examples/demo_asfs.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_asfs.py#L1)
+## 7. Artificial Fish Swarm Algorithm (AFSA)
+-> Demo code: [examples/demo_afsa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_afsa.py#L1)
 ```python
 def func(x):
     x1, x2 = x
     return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2
 
 
-from sko.ASFA import ASFA
+from sko.AFSA import AFSA
 
-asfa = ASFA(func, n_dim=2, size_pop=50, max_iter=300,
+afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300,
             max_try_num=100, step=0.5, visual=0.3,
             q=0.98, delta=0.5)
-best_x, best_y = asfa.run()
+best_x, best_y = afsa.run()
 print(best_x, best_y)
 ```
diff --git a/docs/zh/README.md b/docs/zh/README.md
index 8ea3dcf..cc6b6fe 100644
--- a/docs/zh/README.md
+++ b/docs/zh/README.md
@@ -420,19 +420,19 @@ print('best routine:', best_points, 'best_distance:', best_distance)
 ## 7. 人工鱼群算法
 人工鱼群算法(artificial fish swarm algorithm, AFSA)
 
--> Demo code: [examples/demo_asfs.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_asfs.py#L1)
+-> Demo code: [examples/demo_afsa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_afsa.py#L1)
 ```python
 def func(x):
     x1, x2 = x
     return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2
 
 
-from sko.ASFA import ASFA
+from sko.AFSA import AFSA
 
-asfa = ASFA(func, n_dim=2, size_pop=50, max_iter=300,
+afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300,
             max_try_num=100, step=0.5, visual=0.3,
             q=0.98, delta=0.5)
-best_x, best_y = asfa.run()
+best_x, best_y = afsa.run()
 print(best_x, best_y)
 ```
 
diff --git a/examples/demo_asfs.py b/examples/demo_afsa.py
similarity index 64%
rename from examples/demo_asfs.py
rename to examples/demo_afsa.py
index 3cff988..2f9cde9 100644
--- a/examples/demo_asfs.py
+++ b/examples/demo_afsa.py
@@ -3,10 +3,10 @@ def func(x):
     return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2
 
 
-from sko.ASFA import ASFA
+from sko.AFSA import AFSA
 
-asfa = ASFA(func, n_dim=2, size_pop=50, max_iter=300,
+afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300,
             max_try_num=100, step=0.5, visual=0.3,
             q=0.98, delta=0.5)
-best_x, best_y = asfa.run()
+best_x, best_y = afsa.run()
 print(best_x, best_y)
diff --git a/sko/ASFA.py b/sko/AFSA.py
similarity index 99%
rename from sko/ASFA.py
rename to sko/AFSA.py
index cc223e6..1518db3 100644
--- a/sko/ASFA.py
+++ b/sko/AFSA.py
@@ -96,7 +96,7 @@
 #         return self.best_X, self.best_Y
 
 # %%
-class ASFA:
+class AFSA:
     def __init__(self, func, n_dim, size_pop=50, max_iter=300,
                  max_try_num=100, step=0.5, visual=0.3,
                  q=0.98, delta=0.5):