遗传算法TSP
说明： 用python编译，智能优化算法解决优化组合问题，GA求解旅行商问题
(Genetic algorithm for traveling salesman problem)
(Genetic algorithm for traveling salesman problem)
文件列表:
.DS_Store (6148, 2020-05-01)
.ipynb_checkpoints (0, 2020-05-01)
.ipynb_checkpoints\遗传算法-checkpoint.ipynb (23188, 2020-05-01)
GA.py (8268, 2020-05-01)
img (0, 2020-05-01)
img\.DS_Store (6148, 2020-05-01)
img\result.png (31991, 2020-05-01)
遗传算法.ipynb (23416, 2020-05-01)
.ipynb_checkpoints (0, 2020-05-01)
.ipynb_checkpoints\遗传算法-checkpoint.ipynb (23188, 2020-05-01)
GA.py (8268, 2020-05-01)
img (0, 2020-05-01)
img\.DS_Store (6148, 2020-05-01)
img\result.png (31991, 2020-05-01)
遗传算法.ipynb (23416, 2020-05-01)
# GA-TSP
使用遗传算法解决旅行商问题
# 输入:
10城市坐标为:
- 1: (41, 94);
- 2: (37, 84);
- 3: (54, 67);
- 4: (25, 62);
- 5: (7, ***);
- 6: (2, 99);
- 7: (68, 58);
- 8: (71, 44);
- 9: (54, 62);
- 10: (83, 69);
代码使用临接矩阵的形式保存城市之间的距离信息
# 输出:
1. TSP问题最优路径
2. 最优路径的代价
3. 最优路径代价变化图
# 运行环境:
Python 3.7
# 运行:
```
python GA.py
```
# 运行结果:
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