Merge pull request #276 from hktkzyx/master

Author: geatpy-dev

.flake8

@@ -1,5 +1,4 @@
 [flake8]
 extend-ignore = D1, D204, W292, W293
-docstring-convention = numpy
 max-complexity = 10
 max-line-length = 119

.github/workflows/build_and_test.yml

@@ -0,0 +1,40 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: build-and-test
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+    branches: [master]
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest]
+        python-version: ["3.6"]
+    defaults:
+      run:
+        shell: bash
+    runs-on: ${{ matrix.os }}
+    timeout-minutes: 30
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          pip install cython setuptools
+          pip install -r requirements.txt
+          pip install pytest
+      - name: Build
+        run: python setup.py install
+      - name: Test with pytest
+        run: |
+          python -m geatpy --version
+          pytest

.gitignore

@@ -2,6 +2,9 @@
 .idea/
 .vscode/
 
+# Auto generated folder
+core/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.travis.yml

@@ -1,7 +1,7 @@
-# **Geatpy2** 
+# **Geatpy2**
 The Genetic and Evolutionary Algorithm Toolbox for Python with high performance.
 
-![Travis](https://travis-ci.org/geatpy-dev/geatpy.svg?branch=master)
+[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/geatpy-dev/geatpy/build-and-test)](https://github.com/geatpy-dev/geatpy/actions)
 [![Package Status](https://img.shields.io/pypi/status/geatpy.svg)](https://pypi.org/project/geatpy/)
 ![Python](https://img.shields.io/badge/python->=3.5-green.svg)
 ![Pypi](https://img.shields.io/badge/pypi-2.7.0-blue.svg)
@@ -11,7 +11,7 @@ The Genetic and Evolutionary Algorithm Toolbox for Python with high performance.
 
 ## Introduction
 * **Website (including documentation)**: http://www.geatpy.com
-* **Demo** : https://github.com/geatpy-dev/geatpy/tree/master/geatpy/demo
+* **Demo** : https://github.com/geatpy-dev/geatpy/tree/master/demo
 * **Pypi page** : https://pypi.org/project/geatpy/
 * **Contact us**: http://geatpy.com/index.php/about/
 * **Bug reports**: https://github.com/geatpy-dev/geatpy/issues
@@ -145,7 +145,7 @@ if __name__ == '__main__':
 
 Run the "main.py" and the part of the result is:
 
-![image](https://github.com/geatpy-dev/geatpy/blob/master/geatpy/testbed/moea_test/result/Pareto%20Front%20Plot.svg)
+![image](https://github.com/geatpy-dev/geatpy/blob/master/testbed/moea_test/result/Pareto%20Front%20Plot.svg)
 
 Execution time: 0.3650233745574951 s
 
@@ -213,7 +213,7 @@ if __name__ == '__main__':
 
 Part of the result is:
 
-![image](https://github.com/geatpy-dev/geatpy/blob/master/geatpy/testbed/soea_test/result/Trace%20Plot.svg)
+![image](https://github.com/geatpy-dev/geatpy/blob/master/testbed/soea_test/result/Trace%20Plot.svg)
 
 Execution time: 0.256328821182251 s
 

README.md

@@ -1,8 +1,6 @@
 # -*- coding: utf-8 -*-
-import numpy as np
-import geatpy as ea
+"""Demo.
 
-"""
 min f1 = -25 * (x1 - 2)**2 - (x2 - 2)**2 - (x3 - 1)**2 - (x4 - 4)**2 - (x5 - 1)**2
 min f2 = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x4 - 1)**2 + (x5 - 1)**2
 s.t.
@@ -14,9 +12,13 @@
 (x5 - 3)**2 + x4 - 4 >= 0
 x1,x2,x3,x4,x5 ∈ {0,1,2,3,4,5,6,7,8,9,10}
 """
+import numpy as np
+
+import geatpy as ea
 
 
 class MyProblem(ea.Problem):  # 继承Problem父类
+
     def __init__(self, M=2):
         name = 'MyProblem'  # 初始化name（函数名称，可以随意设置）
         Dim = 5  # 初始化Dim（决策变量维数）
@@ -27,16 +29,27 @@ def __init__(self, M=2):
         lbin = [1] * Dim  # 决策变量下边界（0表示不包含该变量的下边界，1表示包含）
         ubin = [1] * Dim  # 决策变量上边界（0表示不包含该变量的上边界，1表示包含）
         # 调用父类构造方法完成实例化
-        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
+        ea.Problem.__init__(self,
+                            name,
+                            M,
+                            maxormins,
+                            Dim,
+                            varTypes,
+                            lb,
+                            ub,
+                            lbin,
+                            ubin)
 
     def evalVars(self, Vars):  # 目标函数
         x1 = Vars[:, [0]]
         x2 = Vars[:, [1]]
         x3 = Vars[:, [2]]
         x4 = Vars[:, [3]]
         x5 = Vars[:, [4]]
-        f1 = -25 * (x1 - 2) ** 2 - (x2 - 2) ** 2 - (x3 - 1) ** 2 - (x4 - 4) ** 2 - (x5 - 1) ** 2
-        f2 = (x1 - 1) ** 2 + (x2 - 1) ** 2 + (x3 - 1) ** 2 + (x4 - 1) ** 2 + (x5 - 1) ** 2
+        f1 = -25 * (x1 - 2)**2 - (x2 - 2)**2 - (x3 - 1)**2 - (x4 - 4)**2 - (
+            x5 - 1)**2
+        f2 = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x4 - 1)**2 + (x5
+                                                                      - 1)**2
         #        # 利用罚函数法处理约束条件
         #        idx1 = np.where(x1 + x2 < 2)[0]
         #        idx2 = np.where(x1 + x2 > 6)[0]
@@ -48,11 +61,12 @@ def evalVars(self, Vars):  # 目标函数
         #        f1[exIdx] = f1[exIdx] + np.max(f1) - np.min(f1)
         #        f2[exIdx] = f2[exIdx] + np.max(f2) - np.min(f2)
         # 利用可行性法则处理约束条件
-        CV = np.hstack([2 - x1 - x2,
-                            x1 + x2 - 6,
-                            -2 - x1 + x2,
-                            x1 - 3 * x2 - 2,
-                            (x3 - 3) ** 2 + x4 - 4,
-                            4 - (x5 - 3) ** 2 - x4])
+        CV = np.hstack([
+            2 - x1 - x2,
+            x1 + x2 - 6,
+            -2 - x1 + x2,
+            x1 - 3 * x2 - 2, (x3 - 3)**2 + x4 - 4,
+            4 - (x5 - 3)**2 - x4
+        ])
         f = np.hstack([f1, f2])
         return f, CV

geatpy/demo/moea_demo/moea_demo1/MyProblem.py renamed to demo/moea_demo/moea_demo1/MyProblem.py

@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+"""该案例展示了一个离散决策变量的最小化目标的双目标优化问题的求解。问题的定义详见MyProblem.py."""
+from MyProblem import MyProblem  # 导入自定义问题接口
+
+import geatpy as ea  # import geatpy
+
+if __name__ == '__main__':
+    # 实例化问题对象
+    problem = MyProblem()
+    # 构建算法
+    algorithm = ea.moea_NSGA2_templet(
+        problem,
+        ea.Population(Encoding='BG', NIND=50),
+        MAXGEN=200,  # 最大进化代数
+        logTras=0)  # 表示每隔多少代记录一次日志信息，0表示不记录。
+    algorithm.mutOper.Pm = 0.2  # 修改变异算子的变异概率
+    algorithm.recOper.XOVR = 0.9  # 修改交叉算子的交叉概率
+    # 求解
+    res = ea.optimize(algorithm,
+                      verbose=False,
+                      drawing=1,
+                      outputMsg=True,
+                      drawLog=False,
+                      saveFlag=False)
+    print(res)

demo/moea_demo/moea_demo1/main.py

@@ -1,17 +1,19 @@
 # -*- coding: utf-8 -*-
-import numpy as np
-import geatpy as ea
+"""Demo.
 
-"""
 min f1 = X**2
 min f2 = (X - 2)**2
 s.t.
 X**2 - 2.5 * X + 1.5 >= 0
 10 <= Xi <= 10, (i = 1,2,3,...)
 """
+import numpy as np
+
+import geatpy as ea
 
 
 class MyProblem(ea.Problem):  # 继承Problem父类
+
     def __init__(self, M=2):
         name = 'MyProblem'  # 初始化name（函数名称，可以随意设置）
         Dim = 1  # 初始化Dim（决策变量维数）
@@ -22,16 +24,25 @@ def __init__(self, M=2):
         lbin = [1] * Dim  # 决策变量下边界（0表示不包含该变量的下边界，1表示包含）
         ubin = [1] * Dim  # 决策变量上边界（0表示不包含该变量的上边界，1表示包含）
         # 调用父类构造方法完成实例化
-        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
+        ea.Problem.__init__(self,
+                            name,
+                            M,
+                            maxormins,
+                            Dim,
+                            varTypes,
+                            lb,
+                            ub,
+                            lbin,
+                            ubin)
 
     def evalVars(self, Vars):  # 目标函数
-        f1 = Vars ** 2
-        f2 = (Vars - 2) ** 2
+        f1 = Vars**2
+        f2 = (Vars - 2)**2
         #        # 利用罚函数法处理约束条件
         #        exIdx = np.where(Vars**2 - 2.5 * Vars + 1.5 < 0)[0] # 获取不满足约束条件的个体在种群中的下标
         #        f1[exIdx] = f1[exIdx] + np.max(f1) - np.min(f1)
         #        f2[exIdx] = f2[exIdx] + np.max(f2) - np.min(f2)
         # 利用可行性法则处理约束条件
-        CV = -Vars ** 2 + 2.5 * Vars - 1.5
+        CV = -Vars**2 + 2.5 * Vars - 1.5
         ObjV = np.hstack([f1, f2])
         return ObjV, CV

geatpy/demo/moea_demo/moea_demo2/MyProblem.py renamed to demo/moea_demo/moea_demo2/MyProblem.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+"""该案例展示了一个带约束连续决策变量的最小化目标的双目标优化问题的求解。详见MyProblem.py."""
+from MyProblem import MyProblem  # 导入自定义问题接口
+
+import geatpy as ea  # import geatpy
+
+if __name__ == '__main__':
+    # 实例化问题对象
+    problem = MyProblem()
+    # 构建算法
+    algorithm = ea.moea_NSGA2_templet(
+        problem,
+        ea.Population(Encoding='RI', NIND=50),
+        MAXGEN=200,  # 最大进化代数
+        logTras=0)  # 表示每隔多少代记录一次日志信息，0表示不记录。
+    # 求解
+    res = ea.optimize(algorithm,
+                      verbose=False,
+                      drawing=1,
+                      outputMsg=True,
+                      drawLog=False,
+                      saveFlag=False)
+    print(res)

demo/moea_demo/moea_demo2/main.py

@@ -1,9 +1,6 @@
 # -*- coding: utf-8 -*-
-import numpy as np
-import geatpy as ea
+"""一个带约束的多目标背包问题.
 
-"""
-一个带约束的多目标背包问题：
     假设有5类物品，每类物品中包含着四个具体的物品，要求从这五种类别的物品中分别选择一个物品放进背包，
 使背包内的物品总价最高，总体积最小，且背包的总质量不能超过92kg。用矩阵P代表背包中物品的价值；
 矩阵R代表背包中物品的体积；矩阵C代表物品的质量。P,R,C的取值如下:
@@ -17,9 +14,13 @@
 们可以用0, 1, 2, 3来表示具体选择哪件物品。因此染色体可以编码为一个元素属于{0, 1, 2, 3}的1x5Numpy ndarray一维数组，
 比如：[0,0,0,0,0]表示从这五类物品中各选取第一个物品。
 """
+import numpy as np
+
+import geatpy as ea
 
 
 class MyProblem(ea.Problem):  # 继承Problem父类
+
     def __init__(self, M=2):
         name = 'MyProblem'  # 初始化name（函数名称，可以随意设置）
         maxormins = [-1, 1]  # 初始化maxormins（目标最小最大化标记列表，1：最小化该目标；-1：最大化该目标）
@@ -30,20 +31,25 @@ def __init__(self, M=2):
         lbin = [1] * Dim  # 决策变量下边界（0表示不包含该变量的下边界，1表示包含）
         ubin = [1] * Dim  # 决策变量上边界（0表示不包含该变量的上边界，1表示包含）
         # 调用父类构造方法完成实例化
-        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
+        ea.Problem.__init__(self,
+                            name,
+                            M,
+                            maxormins,
+                            Dim,
+                            varTypes,
+                            lb,
+                            ub,
+                            lbin,
+                            ubin)
         # 添加几个属性来存储P、R、C
-        self.P = np.array([[3, 4, 9, 15, 2],
-                           [4, 6, 8, 10, 2.5],
-                           [5, 7, 10, 12, 3],
-                           [3, 5, 10, 10, 2]])
-        self.R = np.array([[0.2, 0.3, 0.4, 0.6, 0.1],
-                           [0.25, 0.35, 0.38, 0.45, 0.15],
-                           [0.3, 0.37, 0.5, 0.5, 0.2],
+        self.P = np.array([[3, 4, 9, 15, 2], [4, 6, 8, 10, 2.5],
+                           [5, 7, 10, 12, 3], [3, 5, 10, 10, 2]])
+        self.R = np.array([[0.2, 0.3, 0.4, 0.6,
+                            0.1], [0.25, 0.35, 0.38, 0.45,
+                                   0.15], [0.3, 0.37, 0.5, 0.5, 0.2],
                            [0.3, 0.32, 0.45, 0.6, 0.2]])
-        self.C = np.array([[10, 13, 24, 32, 4],
-                           [12, 15, 22, 26, 5.2],
-                           [14, 18, 25, 28, 6.8],
-                           [14, 14, 28, 32, 6.8]])
+        self.C = np.array([[10, 13, 24, 32, 4], [12, 15, 22, 26, 5.2],
+                           [14, 18, 25, 28, 6.8], [14, 14, 28, 32, 6.8]])
 
     def evalVars(self, Vars):  # 目标函数
         x = Vars.astype(int)