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scikit-opt

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Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

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# [scikit-opt](https://github.com/guofei9987/scikit-opt)

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Swarm Intelligence in Python  
(Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm, Artificial Fish Swarm Algorithm in Python)

- **Documentation:** [https://scikit-opt.github.io/scikit-opt/#/en/](https://scikit-opt.github.io/scikit-opt/#/en/)
- **文档：** [https://scikit-opt.github.io/scikit-opt/#/zh/](https://scikit-opt.github.io/scikit-opt/#/zh/)  
- **Source code:** [https://github.com/guofei9987/scikit-opt](https://github.com/guofei9987/scikit-opt)
- **Help us improve scikit-opt** [https://www.wjx.cn/jq/50964691.aspx](https://www.wjx.cn/jq/50964691.aspx)

# install
```bash
pip install scikit-opt
```

For the current developer version:
```bach
git clone git@github.com:guofei9987/scikit-opt.git
cd scikit-opt
pip install .
```

# Features
## Feature1: UDF

**UDF** (user defined function) is available now!

For example, you just worked out a new type of `selection` function.  
Now, your `selection` function is like this:  
-> Demo code: [examples/demo_ga_udf.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L1)
```python
# step1: define your own operator:
def selection_tournament(algorithm, tourn_size):
    FitV = algorithm.FitV
    sel_index = []
    for i in range(algorithm.size_pop):
        aspirants_index = np.random.choice(range(algorithm.size_pop), size=tourn_size)
        sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
    algorithm.Chrom = algorithm.Chrom[sel_index, :]  # next generation
    return algorithm.Chrom

```

Import and build ga  
-> Demo code: [examples/demo_ga_udf.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L12)
```python
import numpy as np
from sko.GA import GA, GA_TSP

demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2
ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, prob_mut=0.001,
        lb=[-1, -10, -5], ub=[2, 10, 2], precision=[1e-7, 1e-7, 1])

```
Regist your udf to GA  
-> Demo code: [examples/demo_ga_udf.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L20)
```python
ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3)
```

scikit-opt also provide some operators  
-> Demo code: [examples/demo_ga_udf.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L22)
```python
from sko.operators import ranking, selection, crossover, mutation

ga.register(operator_name='ranking', operator=ranking.ranking). \
    register(operator_name='crossover', operator=crossover.crossover_2point). \
    register(operator_name='mutation', operator=mutation.mutation)
```
Now do GA as usual  
-> Demo code: [examples/demo_ga_udf.py#s5](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L28)
```python
best_x, best_y = ga.run()
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:

-> Demo code: [examples/demo_ga_udf.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L31)
```python
class MyGA(GA):
    def selection(self, tourn_size=3):
        FitV = self.FitV
        sel_index = []
        for i in range(self.size_pop):
            aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size)
            sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
        self.Chrom = self.Chrom[sel_index, :]  # next generation
        return self.Chrom

ranking = ranking.ranking

demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2
my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2],
             precision=[1e-7, 1e-7, 1])
best_x, best_y = my_ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
```

##  feature2: 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
from sko.GA import GA

func = lambda x: x[0] ** 2
ga = GA(func=func, n_dim=1)
ga.run(10)
ga.run(20)
```

## feature3: 4-ways to accelerate
- vectorization
- multithreading
- multiprocessing
- cached

see [https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py)

## feature4: 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)

# Quick start

## 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.
    x1*x2 >= 1
    x1*x2 <= 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)
```python
import numpy as np

def schaffer(p):
    '''
    This function has plenty of local minimum, with strong shocks
    global minimum at (0,0) with value 0
    https://en.wikipedia.org/wiki/Test_functions_for_optimization
    '''
    x1, x2 = p
    part1 = np.square(x1) - np.square(x2)
    part2 = np.square(x1) + np.square(x2)
    return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2)

```

**Step2**: do Genetic Algorithm  
-> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L16)
```python
from sko.GA import GA

ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, prob_mut=0.001, lb=[-1, -1], ub=[1, 1], precision=1e-7)
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
```

-> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L22)
```python
import pandas as pd
import matplotlib.pyplot as plt

Y_history = pd.DataFrame(ga.all_history_Y)
fig, ax = plt.subplots(2, 1)
ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
Y_history.min(axis=1).cummin().plot(kind='line')
plt.show()
```

![Figure_1-1](https://img1.github.io/heuristic_algorithm/ga_1.png)

### 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.  
Here I generate the data randomly as a demo:  
-> Demo code: [examples/demo_ga_tsp.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L1)
```python
import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt

num_points = 50

points_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points
distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')

def cal_total_distance(routine):
    '''The objective function. input routine, return total distance.
    cal_total_distance(np.arange(num_points))
    '''
    num_points, = routine.shape
    return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])

```

**Step2**: do GA  
-> Demo code: [examples/demo_ga_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L19)
```python

from sko.GA import GA_TSP

ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1)
best_points, best_distance = ga_tsp.run()

```

**Step3**: Plot the result:  
-> Demo code: [examples/demo_ga_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L26)
```python
fig, ax = plt.subplots(1, 2)
best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
ax[1].plot(ga_tsp.generation_best_Y)
plt.show()
```

![GA_TPS](https://img1.github.io/heuristic_algorithm/ga_tsp.png)

## 3. PSO(Particle swarm optimization)

### 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
def demo_func(x):
    x1, x2, x3 = x
    return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2

```

**Step2**: do PSO  
-> Demo code: [examples/demo_pso.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L6)
```python
from sko.PSO import PSO

pso = PSO(func=demo_func, n_dim=3, pop=40, max_iter=150, lb=[0, -1, 0.5], ub=[1, 1, 1], w=0.8, c1=0.5, c2=0.5)
pso.run()
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)

```

**Step3**: Plot the result  
-> Demo code: [examples/demo_pso.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L13)
```python
import matplotlib.pyplot as plt

plt.plot(pso.gbest_y_hist)
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`

More over, we have an animation:  
![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)**

## 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
demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2

```
**Step2**: do SA  
-> Demo code: [examples/demo_sa.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L3)
```python
from sko.SA import SA

sa = SA(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, L=300, max_stay_counter=150)
best_x, best_y = sa.run()
print('best_x:', best_x, 'best_y', best_y)

```

**Step3**: Plot the result  
-> Demo code: [examples/demo_sa.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L10)
```python
import matplotlib.pyplot as plt
import pandas as pd

plt.plot(pd.DataFrame(sa.best_y_history).cummin(axis=0))
plt.show()

```
![sa](https://img1.github.io/heuristic_algorithm/sa.png)

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)
### 4.2 SA for TSP
**Step1**: oh, yes, define your problems. To boring to copy this step.

**Step2**: DO SA for TSP  
-> Demo code: [examples/demo_sa_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L21)
```python
from sko.SA import SA_TSP

sa_tsp = SA_TSP(func=cal_total_distance, x0=range(num_points), T_max=100, T_min=1, L=10 * num_points)

best_points, best_distance = sa_tsp.run()
print(best_points, best_distance, cal_total_distance(best_points))
```

**Step3**: plot the result  
-> Demo code: [examples/demo_sa_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L28)
```python
from matplotlib.ticker import FormatStrFormatter

fig, ax = plt.subplots(1, 2)

best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(sa_tsp.best_y_history)
ax[0].set_xlabel("Iteration")
ax[0].set_ylabel("Distance")
ax[1].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1],
           marker='o', markerfacecolor='b', color='c', linestyle='-')
ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
ax[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
ax[1].set_xlabel("Longitude")
ax[1].set_ylabel("Latitude")
plt.show()

```
![sa](https://img1.github.io/heuristic_algorithm/sa_tsp.png)

More: Plot the animation:

![sa](https://img1.github.io/heuristic_algorithm/sa_tsp1.gif)  
↑**see [examples/demo_sa_tsp.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py)**

## 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#L17)
```python
from sko.ACA import ACA_TSP

aca = ACA_TSP(func=cal_total_distance, n_dim=num_points,
              size_pop=50, max_iter=200,
              distance_matrix=distance_matrix)

best_x, best_y = aca.run()

```

![ACA](https://img1.github.io/heuristic_algorithm/aca_tsp.png)

## 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=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)

```

![IA](https://img1.github.io/heuristic_algorithm/ia2.png)

## 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.AFSA import AFSA

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 = afsa.run()
print(best_x, best_y)
```

# Projects using scikit-opt

- [Yu, J., He, Y., Yan, Q., & Kang, X. (2021). SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation. IEEE Transactions on Information Forensics and Security, 16, 5093-5107.](https://ieeexplore.ieee.org/abstract/document/9607026/)
- [Zhen, H., Zhai, H., Ma, W., Zhao, L., Weng, Y., Xu, Y., ... & He, X. (2021). Design and tests of reinforcement-learning-based optimal power flow solution generator. Energy Reports.](https://www.sciencedirect.com/science/article/pii/S2352484721012737)
- [Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494.](https://www.sciencedirect.com/science/article/pii/S016792362100004X)
- [Tang, H. K., & Goh, S. K. (2021). A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing. arXiv preprint arXiv:2104.08564.](https://arxiv.org/abs/2104.08564)
- [Wu, G., Li, L., Li, X., Chen, Y., Chen, Z., Qiao, B., ... & Xia, L. (2021). Graph embedding based real-time social event matching for EBSNs recommendation. World Wide Web, 1-22.](https://link.springer.com/article/10.1007/s11280-021-00934-y)
- [Pan, X., Zhang, Z., Zhang, H., Wen, Z., Ye, W., Yang, Y., ... & Zhao, X. (2021). A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function. Sensors and Actuators B: Chemical, 342, 129982.](https://www.sciencedirect.com/science/article/abs/pii/S0925400521005517)
- [Castella Balcell, M. (2021). Optimization of the station keeping system for the WindCrete floating offshore wind turbine.](https://upcommons.upc.edu/handle/2117/350262)
- [Zhai, B., Wang, Y., Wang, W., & Wu, B. (2021). Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions. arXiv preprint arXiv:2107.14406.](https://arxiv.org/abs/2107.14406)
- [Yap, X. H. (2021). Multi-label classification on locally-linear data: Application to chemical toxicity prediction.](https://etd.ohiolink.edu/apexprod/rws_olink/r/1501/10?clear=10&p10_accession_num=wright162901936395651)
- [Gebhard, L. (2021). Expansion Planning of Low-Voltage Grids Using Ant Colony Optimization Ausbauplanung von Niederspannungsnetzen mithilfe eines Ameisenalgorithmus.](https://ad-publications.cs.uni-freiburg.de/theses/Master_Lukas_Gebhard_2021.pdf)
- [Ma, X., Zhou, H., & Li, Z. (2021). Optimal Design for Interdependencies between Hydrogen and Power Systems. IEEE Transactions on Industry Applications.](https://ieeexplore.ieee.org/abstract/document/9585654)
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