version 0.5.0: Differential Evolution is available

guofei9987 · Dec 2, 2019 · 10bf651 · 10bf651
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diff --git a/README.md b/README.md
@@ -53,8 +53,7 @@ pip install .
 ```
 
 ## News:
-All algorithms will be available on ~~TensorFlow/Spark~~ **pytorch** on version 0.4, getting parallel performance.  
-DE(Differential Evolution Algorithm) will be complete on version 0.5  
+All algorithms will be available on ~~TensorFlow/Spark~~ **pytorch** on version 1.0.0, getting parallel performance.  
 Have fun!
 
 
@@ -130,7 +129,50 @@ ga.run(20)
 ```
 
 # Quick start
-## 1. Genetic Algorithm
+
+## 1. Differential Evolution
+**Step1**：define your problem  
+-> Demo code: [examples/demo_de.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L1)
+```python
+'''
+min f(x1, x2, x3) = x1^2 + x2^2 + x3^2
+s.t.
+    x1x2 >= 1
+    x1x2 <= 5
+    x2+x3 = 1
+    0 <= x1, x2, x3 <= 5
+'''
+
+
+def obj_func(p):
+    x1, x2, x3 = p
+    return x1 ** 2 + x2 ** 2 + x3 ** 2
+
+
+constraint_eq = [
+    lambda x: 1 - x[1] - x[2]
+]
+
+constraint_ueq = [
+    lambda x: 1 - x[0] * x[1],
+    lambda x: x[0] * x[1] - 5
+]
+
+```
+
+**Step2**: do Differential Evolution  
+-> Demo code: [examples/demo_de.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L25)
+```python
+from sko.DE import DE
+
+de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5],
+        constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)
+
+best_x, best_y = de.run()
+print('best_x:', best_x, '\n', 'best_y:', best_y)
+```
+
+## 2. Genetic Algorithm
 
 **Step1**：define your problem  
 -> Demo code: [examples/demo_ga.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L1)
@@ -150,7 +192,7 @@ def schaffer(p):
 
 ```
 
-**Step2**: do GA  
+**Step2**: do Genetic Algorithm  
 -> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L14)
 ```python
 from sko.GA import GA
@@ -176,7 +218,7 @@ plt.show()
 
 ![Figure_1-1](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_1.png?raw=true)
 
-### 1.1 Genetic Algorithm for TSP(Travelling Salesman Problem)
+### 2.2 Genetic Algorithm for TSP(Travelling Salesman Problem)
 Just import the `GA_TSP`, it overloads the `crossover`, `mutation` to solve the TSP
 
 **Step1**: define your problem. Prepare your points coordinate and the distance matrix.  
@@ -227,9 +269,9 @@ plt.show()
 ![GA_TPS](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_tsp.png?raw=true)
 
 
-## 2. PSO(Particle swarm optimization)
+## 3. PSO(Particle swarm optimization)
 
-### 2.1 PSO with constraint
+### 3.1 PSO with constraint
 **Step1**: define your problem:  
 -> Demo code: [examples/demo_pso.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L1)
 ```python
@@ -268,16 +310,16 @@ plt.show()
 ![pso_ani](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/pso.gif?raw=true)  
 ↑**see [examples/demo_pso_ani.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)**
 
-### 2.2 PSO without constraint
+### 3.2 PSO without constraint
 -> Demo code: [examples/demo_pso.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L19)
 ```python
 pso = PSO(func=demo_func, dim=3)
 fitness = pso.run()
 print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)
 ```
 
-## 3. SA(Simulated Annealing)
-### 3.1 SA for multiple function
+## 4. SA(Simulated Annealing)
+### 4.1 SA for multiple function
 **Step1**: define your problem  
 -> Demo code: [examples/demo_sa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L1)
 ```python
@@ -309,7 +351,7 @@ plt.show()
 
 
 Moreover, scikit-opt provide 3 types of Simulated Annealing: Fast, Boltzmann, Cauchy. See [more sa](https://scikit-opt.github.io/scikit-opt/#/en/more_sa)
-### 3.2 SA for TSP
+### 4.2 SA for TSP
 **Step1**: oh, yes, define your problems. To boring to copy this step.  
 
 **Step2**: DO SA for TSP  
@@ -355,7 +397,7 @@ More: Plot the animation:
 
 
 
-## 4. ACA (Ant Colony Algorithm) for tsp 
+## 5. ACA (Ant Colony Algorithm) for tsp 
 -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L23)
 ```python
 from sko.ACA import ACA_TSP
@@ -371,13 +413,13 @@ best_x, best_y = aca.run()
 ![ACA](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/aca_tsp.png?raw=true)
 
 
-## 5. immune algorithm (IA)
+## 6. immune algorithm (IA)
 -> Demo code: [examples/demo_ia.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ia.py#L6)
 ```python
 
 from sko.IA import IA_TSP
 
-ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=2000, prob_mut=0.2,
+ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=800, prob_mut=0.2,
                 T=0.7, alpha=0.95)
 best_points, best_distance = ia_tsp.run()
 print('best routine:', best_points, 'best_distance:', best_distance)
@@ -386,7 +428,7 @@ 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)
 
-## 6. artificial fish swarm algorithm (AFSA)
+## 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)
 ```python
 def func(x):

diff --git a/docs/en/README.md b/docs/en/README.md
@@ -110,7 +110,50 @@ ga.run(20)
 ```
 
 # Quick start
-## 1. Genetic Algorithm
+
+## 1. Differential Evolution
+**Step1**：define your problem  
+-> Demo code: [examples/demo_de.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L1)
+```python
+'''
+min f(x1, x2, x3) = x1^2 + x2^2 + x3^2
+s.t.
+    x1x2 >= 1
+    x1x2 <= 5
+    x2+x3 = 1
+    0 <= x1, x2, x3 <= 5
+'''
+
+
+def obj_func(p):
+    x1, x2, x3 = p
+    return x1 ** 2 + x2 ** 2 + x3 ** 2
+
+
+constraint_eq = [
+    lambda x: 1 - x[1] - x[2]
+]
+
+constraint_ueq = [
+    lambda x: 1 - x[0] * x[1],
+    lambda x: x[0] * x[1] - 5
+]
+
+```
+
+**Step2**: do Differential Evolution  
+-> Demo code: [examples/demo_de.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L25)
+```python
+from sko.DE import DE
+
+de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5],
+        constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)
+
+best_x, best_y = de.run()
+print('best_x:', best_x, '\n', 'best_y:', best_y)
+```
+
+## 2. Genetic Algorithm
 
 **Step1**：define your problem  
 -> Demo code: [examples/demo_ga.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L1)
@@ -130,7 +173,7 @@ def schaffer(p):
 
 ```
 
-**Step2**: do GA  
+**Step2**: do Genetic Algorithm  
 -> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L14)
 ```python
 from sko.GA import GA
@@ -156,7 +199,7 @@ plt.show()
 
 ![Figure_1-1](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_1.png?raw=true)
 
-### 1.1 Genetic Algorithm for TSP(Travelling Salesman Problem)
+### 2.2 Genetic Algorithm for TSP(Travelling Salesman Problem)
 Just import the `GA_TSP`, it overloads the `crossover`, `mutation` to solve the TSP
 
 **Step1**: define your problem. Prepare your points coordinate and the distance matrix.  
@@ -207,9 +250,9 @@ plt.show()
 ![GA_TPS](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/ga_tsp.png?raw=true)
 
 
-## 2. PSO(Particle swarm optimization)
+## 3. PSO(Particle swarm optimization)
 
-### 2.1 PSO with constraint
+### 3.1 PSO with constraint
 **Step1**: define your problem:  
 -> Demo code: [examples/demo_pso.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L1)
 ```python
@@ -248,16 +291,16 @@ plt.show()
 ![pso_ani](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/pso.gif?raw=true)  
 ↑**see [examples/demo_pso_ani.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)**
 
-### 2.2 PSO without constraint
+### 3.2 PSO without constraint
 -> Demo code: [examples/demo_pso.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L19)
 ```python
 pso = PSO(func=demo_func, dim=3)
 fitness = pso.run()
 print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)
 ```
 
-## 3. SA(Simulated Annealing)
-### 3.1 SA for multiple function
+## 4. SA(Simulated Annealing)
+### 4.1 SA for multiple function
 **Step1**: define your problem  
 -> Demo code: [examples/demo_sa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L1)
 ```python
@@ -289,7 +332,7 @@ plt.show()
 
 
 Moreover, scikit-opt provide 3 types of Simulated Annealing: Fast, Boltzmann, Cauchy. See [more sa](https://scikit-opt.github.io/scikit-opt/#/en/more_sa)
-### 3.2 SA for TSP
+### 4.2 SA for TSP
 **Step1**: oh, yes, define your problems. To boring to copy this step.  
 
 **Step2**: DO SA for TSP  
@@ -335,7 +378,7 @@ More: Plot the animation:
 
 
 
-## 4. ACA (Ant Colony Algorithm) for tsp 
+## 5. ACA (Ant Colony Algorithm) for tsp 
 -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L23)
 ```python
 from sko.ACA import ACA_TSP
@@ -351,13 +394,13 @@ best_x, best_y = aca.run()
 ![ACA](https://github.com/guofei9987/pictures_for_blog/blob/master/heuristic_algorithm/aca_tsp.png?raw=true)
 
 
-## 5. immune algorithm (IA)
+## 6. immune algorithm (IA)
 -> Demo code: [examples/demo_ia.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ia.py#L6)
 ```python
 
 from sko.IA import IA_TSP
 
-ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=2000, prob_mut=0.2,
+ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=800, prob_mut=0.2,
                 T=0.7, alpha=0.95)
 best_points, best_distance = ia_tsp.run()
 print('best routine:', best_points, 'best_distance:', best_distance)
@@ -366,7 +409,7 @@ 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)
 
-## 6. artificial fish swarm algorithm (AFSA)
+## 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)
 ```python
 def func(x):