version: 0.6.1, PSO with nonlinear constraint is available.

README.md

@@ -294,7 +294,7 @@ plt.show()
 
 ## 3. PSO(Particle swarm optimization)
 
-### 3.1 PSO with constraint
+### 3.1 PSO 
 **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
@@ -328,6 +328,21 @@ plt.show()
 
 ![PSO_TPS](https://img1.github.io/heuristic_algorithm/pso.png)
 
+### 3.2 PSO with nonlinear constraint
+
+If you need nolinear constraint like `(x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2<=0`  
+Codes are like this:
+```python
+constraint_ueq = (
+    lambda x: (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2
+    ,
+)
+pso = PSO(func=demo_func, n_dim=2, pop=40, max_iter=max_iter, lb=[-2, -2], ub=[2, 2]
+          , constraint_ueq=constraint_ueq)
+```
+
+Note that, you can add more then one nonlinear constraint. Just add it to `constraint_ueq`
+
 
 ![pso_ani](https://img1.github.io/heuristic_algorithm/pso.gif)  
 ↑**see [examples/demo_pso_ani.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)**

docs/en/args.md

@@ -56,7 +56,7 @@ help(sko.AFSA.AFSA)
 | w         | 0\.8 | inertia weight  |
 | c1        | 0\.5 | cognitive parameter |
 | c2        | 0\.5 | social parameter |
-
+| constraint\_ueq | tuple()    | unequal constraint                  |
 
 ### DE
 

docs/zh/README.md

@@ -262,7 +262,7 @@ plt.show()
 ## 3. 粒子群算法
 (PSO, Particle swarm optimization)
 
-### 3.1 带约束的粒子群算法
+### 3.1 粒子群算法
 **第一步**，定义问题  
 -> Demo code: [examples/demo_pso.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L1)
 ```python
@@ -296,6 +296,21 @@ plt.show()
 ![pso_ani](https://img1.github.io/heuristic_algorithm/pso.gif)  
 ↑**see [examples/demo_pso.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)**
 
+### 3.2 带非线性约束的粒子群算法
+
+### 3.2 PSO with nonlinear constraint
+
+加入你的非线性约束是个圆内的面积 `(x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2<=0`  
+这样写代码:
+```python
+constraint_ueq = (
+    lambda x: (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2
+    ,
+)
+pso = PSO(func=demo_func, n_dim=2, pop=40, max_iter=max_iter, lb=[-2, -2], ub=[2, 2]
+          , constraint_ueq=constraint_ueq)
+```
+可以有多个非线性约束，向 `constraint_ueq` 加就行了。
 
 ## 4. 模拟退火算法
 (SA, Simulated Annealing)

docs/zh/args.md

@@ -56,7 +56,7 @@ help(sko.AFSA.AFSA)
 | w         | 0\.8 | 惯性权重     |
 | c1        | 0\.5 | 个体记忆     |
 | c2        | 0\.5 | 集体记忆     |
-
+| constraint\_ueq | 空元组    | 非线性约束                  |
 
 ### DE
 

sko/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.5.9'
+__version__ = '0.6.1'
 
 from . import DE, GA, PSO, SA, ACA, AFSA, IA