semvec/pypi-packages huggingface dataset:
enhanced-political-optimizer 1.0.0
Solve global optimization problems.
License: MIT

## How to use the package.

To use the package you need to provide four things to the PO methods

- Optimization function, which the is function that you are trying to optimize. Our method can cater to constraints as well by using static penalty. An example is given below.
- Number of dimensions of the problem
- Lower boundary
- Upper boundary

**Note** The boundaries can be scalars or arrays. The example below uses arrays because the
boundaries are different for different variables, if they are same, use scalar values.

```
import numpy as np
import matplotlib.pyplot as plt


## optimization function.
def TCSD(x: np.ndarray):
    x1 = x[0]
    x2 = x[1]
    x3 = x[2]
    z = (x3 + 2) * x2 * (x1 * x1)

    c = [
        1 - ((x2 * x2 * x2 * x3)) / (71785 * x1 * x1 * x1 * x1),
        ((4 * x2 * x2 - (x1 * x2)) / (12566 * (x2 * x1 * x1 * x1 - (x1 ** 4))))
        + (1 / (5108 * x1 * x1))
        - 1,
        1 - ((140.45 * x1) / (x2 * x2 * x3)),
        ((x1 + x2) / 1.5) - 1,
    ]
    cmaxes = [x if x > 0 else 0 for x in c]
    c_np_array = np.array(cmaxes)
    c_np_array = np.power(c_np_array, 2)
    c_np_array = (10 ** 10) * c_np_array
    sumofarray = np.sum(c_np_array)
    return z + sumofarray


scores = [0 for x in range(RUNS)]

for i in range(RUNS):
    # matlab's rng("shuffle") seeds with the system's default time, python's random.seed has that as default val
    np.random.seed(1)
    # implement political optimizer.

    objectiveFunction = TCSD
    dim = 3 # dimentions
    lb = [0.05, 0.25, 2.00] # upper boundary
    ub = [2.00, 1.30, 15.00] # lower boundary
    (leaderScore, leaderPosition, convergenceCurve) = PO(lb, ub, dim, objectiveFunction)
    print(leaderScore)
    print(leaderPosition)

```